Content uploaded by Amin Golkarfard
Author content
All content in this area was uploaded by Amin Golkarfard on Apr 21, 2025
Content may be subject to copyright.
C0 5 (2), 1136-1147 (2025)
1136
Current Opinion
Current
Opinion
CO 5 (2), 1136− 1147 (2025) Current Opinion
Received 15 January
2025 |
Revised 22 February
2025 |
Accepted 16 March
2025 |
Online Available 16 April
2025
https://doi.org/10.52845/currentopinion.v5i2.363
OPEN ACCESS JOURNAL
ISSN (O) 2795- 935X
Original Article
Ethical Challenges of AI Integration in Architecture and Built Environment
Amin Golkarfard1, Sahar Sadeghmalakabadi2, Shima Talebian3, Sepideh Basirat4,
Navid Golchin5
1Civil and Environmental Engineering Department, University of Louisville, Kentucky, USA
ORCiD: 0009-0003-8306-6477
2Geography Graduate Group, University of California, Davis, USA
ORCiD: 0009-0002-3450-4031
3Department of Interior Architecture, University of North Carolina at Greensboro (UNCG),
Greensboro, NC 27412, United States
4Master of Business Administration, Department of Business Administration, University of the
Potomac, Washington, DC 20005, United States
ORCiD: 0009-0007-3288-1657
5School of Architecture, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, NV
89154, USA
ORCiD: 0000-0001-9464-2378
*Corresponding Author: Amin Golkarfard
Abstract
Artificial intelligence is transforming the way cities operate by increasing efficiency and sustainability.
Smart cities use artificial intelligence (AI) to optimize traffic flow, reduce energy usage, and improve public
services. AI-powered systems process massive volumes of data in real time to improve urban planning and
resource allocation. However, there are certain obstacles, such as data protection, ethical considerations, and
the potential of employment displacement. This study investigates how AI contributes to smart cities and
the limitations that must be overcome. Understanding these aspects enables urban planners to develop AI-
powered solutions that promote sustainable and equitable city growth.
Keywords: Artificial Intelligence (AI), Smart Cities, Urban Resilience, Architecture, Sustainability,
Urban Planning.
Introduction
The idea of cities that are smart is developing as
new technology in urban infrastructure becomes
available. Modern technology is becoming an
important instrument for creating the future of
cities as they address difficulties such as resource
management, environmental protection and rapid
population grows. These technologies improve
how cities operate by making them more efficient
and sustainable while providing new ways to
solve common urban issues. As cities around the
world grow larger more are adopting smart
systems to improve services such as transportation
energy use and waste management.
Beyond basic automation, artificial intelligence
(AI) contributes to smart cities by enabling
autonomous resource management, real-time
decision-making, and predictive analysis, all of
which lead to more sustainable urban
development. As cities try to minimize their
carbon footprints and become more resilient to
C0 5 (2), 1136-1147 (2025)
1137
Amin Golkarfard et al.
climate change, artificial intelligence provides
innovative solutions to enhance energy efficiency,
reduce waste and improve people overall quality
of life (Balcı et al., 2025). For example, AI may
evaluate enormous volumes of data from sensors
implanted in the urban fabric to optimize traffic
flow, monitor air quality and control energy usage
which is lowering cities' environmental impact
(Khan et al., 2023).
Generative AI is rapidly evolving toward next-
generation AI applications centered around
autonomous adaptation and creativity. (Mahmoud
& Mohammadabadi, 2025) outlines an
evolutionary strategy for AI systems in city
planning around self-generating architectural
forms, infrastructure predictive maintenance, and
adaptive urban mobility options. This strategy
powered by generative AI reinforces the ability of
smart cities to maximize spatial arrangement,
reduce carbon emissions, and enhance urban
habitability through dynamically learned
adaptation to real-time environmental dynamics.
However, despite its potential, using AI in smart
cities presents problems. One of the main
challenges is the ethical implications of AI,
particularly in terms of privacy and data security.
As AI systems collect and analyze vast volumes
of personal data, protecting people' privacy
becomes increasingly important (Muralidhara Rao
et al., 2022). Furthermore, the dependence on AI
raises worries about technological unemployment
and the demand for a competent workforce to
operate and maintain these systems (Yigitcanlar &
Cugurullo, 2020).
Figure 1 Overview of AI Workflow. Source: Author.
Figure 1. A simplified overview of the AI
workflow, illustrating the key stages from data
collection and preprocessing to model training,
testing, inference, deployment, and ongoing
maintenance.
Despite these challenges the application of AI in
smart cities has a useful future. The continuous
development of AI technologies such as
blockchain and machine learning is enabling
smarter, more sustainable cities (Mrabet & Sliti,
2024). As AI systems advance, they will have a
substantial impact on the future of urban life by
improving the livability, efficiency, and
environmental friendliness of cities. By
employing AI to improve urban operations and
reduce environmental impact, cities may play a
big part in advancing sustainability in the twenty-
first century (Zafar, 2024).
In conclusion, the development of artificial
intelligence will determine how smart cities
develop in the future. Opportunities for more
sustainable cities arise as AI's capacity to alter
urban environments advances. To guarantee that
the benefits are shared equally among all citizens
of cities, it is imperative to address the ethical and
social issues surrounding AI (Bibri et al., 2023).
1. The Importance of AI in Sustainable Urban
Development
Cities can expand more intelligently and
sustainably with the aid of artificial intelligence.
Cities become more ecologically friendly and
Data
Collection
Data
Preprocessing
Model
Selection Monitoring &
Maintenance
Deployment
Feedback
Loop
Inference /
Prediction
Testing &
Validation
Training
C0 5 (2), 1136-1147 (2025)
1138
Amin Golkarfard et al.
efficient as a result. AI is capable of processing
large amounts of data rapidly. This aids city
planners in making better choices that enhance
people's quality of life. By better controlling the
usage of electricity, AI also contributes to energy
conservation. Smart networks ensure that energy
is used where it is most required and is not wasted
(Babu et al., 2023). AI can enhance transportation
as well. It improves the efficiency of public
transportation and aids in traffic flow regulation
(Zafar, 2024). AI helps monitor air quality,
manage waste and reduce pollution when
combined with smart technologies like the
Internet of Things (Herath & Mittal, 2022). Cities
may expand using AI while conserving resources
and preserving the environment.
Architecture has perpetually been a reflection of
human ambitions and culture, and the preservation
of ancient monuments is fundamental to
maintaining such connections. (Roya Nazari
Najafabadi et al., 2024) underscore the importance
of ecological restoration in maintaining the
structural and cultural integrity of such sites as the
Chogha Zanbil ziggurat. According to their study,
ecological restoration enhances the integration of
historic buildings with the environment while
maintaining their architectural, cultural, and
religious significance. AI helps preserve cultural
monuments by analyzing structural integrity,
predicting deterioration, automating restoration
processes, and enhancing conservsation efforts
with data.
ChronoGAN, a time-series generation model,
enhances this predictive power of AI in city
planning by simulating traffic patterns, energy
consumption, and weather fluctuations. By
integrating temporal interdependencies into the
produced data, ChronoGAN enhances long-term
forecasting performance of crucial critical smart
city services, such as energy optimization and
disaster preparedness (EskandariNasab et al.,
2024). This simulation-based approach with AI
resolves uncertainty and promotes more
sustainable and resilient cities.
The integration of AI and big data financial
technologies is improving urban economic
resource distribution. Based on consumption
patterns analysis, AI-based financial models guide
investment, optimize municipal budgeting, and
facilitate financial inclusion in underbanked urban
communities (Pazouki, Behdad, et al., 2025).
Artificial intelligence applications in green
architecture range from energy efficiency to
carbon reduction throughout the building's life
cycle. (Wang et al., 2024) present a life cycle
assessment (LCA) technique for comparing the
carbon footprint of buildings based on design,
material consumption during construction, and
energy use. The comprehensive approach enables
urban planners to implement AI-based carbon
accounting and design low-carbon cities that meet
global sustainability criteria.
The integration of dual RIS-supported WSNs
enhances energy efficiency in smart cities. With
fuzzy deep reinforcement learning, the networks
streamline data transmission, reduce latency, and
optimize overall QoS. The innovation cuts energy
consumption noticeably while maintaining secure
data exchange between interconnected systems, a
necessity for scalable smart infrastructure in cities
(Khatami et al., 2025). As cities transition towards
sustainability, such adaptive WSNs offer vital
pathways to achieving more sustainable and low-
energy urban systems.
The inclusion of new technologies is a vital
component in the evolution of smart cities. One
example of this technology is Radio Frequency
Identification (RFID) which enhances the
operations of the supply chain with increased
efficiency and lower costs. (Saremi et al., 2013)
assert that the implementation of RFID in
Malaysia has improved real-time location tracking
and inventory management. The use of RFID
within smart cities can incorporate resources and
logistics and make the city even smarter.
Artificial intelligence (AI)-based multi-criteria
decision-making (MCDM) models are
revolutionizing the digital economy as well as
urban governance. (Entezami et al., 2025) point
towards the ability of AI to evaluate economic,
environmental, and social criteria to guide urban
development policies. This AI-based system
allows city planners to make data-informed
decisions, identify sustainable investments, and
ensure equitable access to urban resources, thus
fostering more resilient and inclusive urban
environments.
Intelligent freight transport is essential in enabling
smart cities to realize supply chain efficiency and
C0 5 (2), 1136-1147 (2025)
1139
Amin Golkarfard et al.
reduced carbon footprints. (Espahbod, 2020)
emphasizes the potential of AI and IoT in the
governance of intelligent freight systems by
augmenting real-time routing, load optimization,
and delivery productivity. With this integration,
fuel consumption decreases and traffic congestion
eases, resulting in a more sustainable and resilient
urban supply chain network, which plays a
significant role in meeting growing urban
populations' demands.
Artificial intelligence ability to enhance
architectural applications also appears in urban
planning and campus planning. Deep learning
models, for instance, have exhibited a high ability
to classify architectural images accurately, which
is crucial in record keeping of historical buildings,
monitoring renovations, and improving the
mapping of cities. (Karkehabadi &
Sadeghmalakabadi, 2024) researched to
demonstrate how AI could analyze architectural
trends in the UC Davis campus to identify spatial
inefficiencies and optimize pedestrian paths. The
technology allows city planners to create more
pedestrian-friendly, resource-efficient spaces
through an understanding of real-time information
regarding infrastructure usage.
Technology improves resource management,
supports environmental protection and energy
efficiency which all contribute to sustainable
urban growth. (Talebian et al., 2025) explain that
smart building systems can lower energy
consumption by optimizing heating and cooling
functions. These systems adjust energy use based
on occupancy and external conditions by
collecting and analyzing real-time data from
various sensors. This process maintains indoor
comfort while reducing carbon emissions and
preventing energy waste. By increasing building
efficiency and minimizing resource use,
technology plays an important role in advancing
sustainable urban development (Talebian et al.,
2025).
1. Challenges Cities Face
Cities face substantial challenges that restrict their
ability to develop in a way that is beneficial.
Population increase is one issue. Each day, more
individuals relocate to urban areas. According to
(Leal Filho et al., 2024), this raises the demand for
a place to live and essential services. Cities must
grow while providing for the needs of their
citizens. Climate change is another issue. Extreme
weather events as heat waves, storms and floods
occur in many cities. (NASA’s Scientific
Visualization Studio, 2025) reports that the global
temperature has risen by 1.28 °C and 2.3 °F
(Fig2). Cities can become more resilient to
catastrophes by using AI to better foresee these
occurrences (Bibri et al., 2023). Resource
management is another issue that cities face.
Living sustainably is made more difficult by
issues with water shortages and overuse of energy.
Figure 2, Climate Change Over Years. Source: (NASA’s Scientific Visualization Studio, 2025)
C0 5 (2), 1136-1147 (2025)
1140
Figure 1 shows global temperature rise of 1.28 °C
(2.3 °F) as reported by NASA's Scientific
Visualization Studio (2025).
Urban planners are increasingly faced with
ensuring soil stability in infrastructure
development, especially in fine-grained soil. A
comparative study by (Barati-Nia et al., 2025) of
the efficiency of SHANSEP and recompression
methods in improving soil resistance to cyclic
stress conditions is presented. Results suggest how
sustainable urban infrastructure development may
be facilitated through the guarantee that
foundations are stable even under extreme climate
or seismic activity.
Volatility in truckload spot markets of
transportation creates inefficiencies that burden
urban freight logistics. (Haughton et al., 2022)
analyze the "price of anarchy" effect, where
decentralized carrier decision-making produces
non-optimal outcomes. This refers to the need for
AI-driven coordination in urban logistics where
centralized platforms could optimize delivery
efficiency, make prices stable, and reduce
avoidable emissions from inefficient route
planning and redundant truckloads.
2. Purpose and Scope of This Article
The goal of this study is to investigate the role of
artificial intelligence in the development of smart
cities. It aims to reveal the advantages of AI in
urban administration such as traffic optimization
reduced energy usage and improved public
services. The report also discusses the ethical
issues and concerns involved with AI
implementation such as data security and
employment loss. By tackling these concerns the
research provides insights on how AI might be
integrated into urban planning to produce more
sustainable and efficient communities.
Table 1: Applications of AI in Smart Cities
Application
Description
Traffic Management
AI analyzes traffic data to reduce congestion and optimize routes
Energy Efficiency
AI monitors power usage and optimizes distribution
Waste Management
AI predicts waste collection schedules and improves recycling rates
Public Safety
AI enhances surveillance and emergency response times
3. Challenges and Ethical Considerations
The use of artificial intelligence in smart cities
raises several ethical problems and barriers to
achieving sustainable urban development.
Concerns about data security and privacy, bias in
decisions, job ramifications and unequal access to
digital services are just a few of these challenges.
Walkability is one of the most important aspects
of sustainable urban planning, particularly where
cities are densely populated. Walkability corridor
studies, for example, for Saadatabad Square in
Tehran, demonstrate the usefulness of AI-based
applications for assessing pedestrian comfort and
safety. (Oskooie et al., 2023) describe the
capability of AI to optimize corridor designs
based on pedestrian density, air quality, and
accessibility considerations. This data-driven
approach minimizes city traffic and ensures
equitable distribution of public space in line with
the principles of inclusive and ethical urban
development.
AI plays a vital role in mitigation of urban freight
logistics inefficiencies. (Espahbod, 2024) points
towards the dynamics of trucking freight spot
markets, and it demonstrates how AI-based
logistics can reduce redundancies on routes,
streamline delivery schedules, and reduce carbon
footprint through maximizing real-time load
distribution and preventing empty miles.
AI-based FinTech models are a cause for concern
in city economies, particularly with regards to
data privacy and algorithmic transparency.
(Pazouki, Metamax, et al., 2025) emphasize the
need for fair regulation of AI in order to prevent
discriminatory outcomes and encourage ethical
practices in finance systems within smart cities.
Decentralized multi-agent learning introduces
novel communication efficiency problems,
particularly in urban complex systems. A
workload-balancing approach by (Sajjadi
Mohammadabadi, Yang, et al., 2024) optimizes
task distribution among AI agents, minimizing
network congestion and speeding up task
execution in smart city applications. The model
promotes equitable resource allocation and
addresses ethical concerns related to AI bias,
system overload, and data access in urban
governance.
C0 5 (2), 1136-1147 (2025)
1141
Amin Golkarfard et al.
Artificial intelligence has a double-edged
contribution towards financial market stability,
with its effects on sustainable growth and
speculative bubbles. While AI becomes more
integrated into urban economies, discrimination
between authentic value creation in markets and
harmful hype is necessary to prevent economic
fluctuations. Financial regulation through AI can
analyze prospective speculative patterns and
alleviate associated risks, enhancing economic
resilience in intelligent cities (Ahmadirad, 2024a).
Driven by AI smart cities must provide data
security and privacy. People provide sensors and
digital technologies with a vast amount of data.
When data is accessed or exploited, poor data
management can lead to privacy violations and
cybersecurity breaches (Muralidhara Rao et al.,
2022). Enacting rigorous data protection
legislation and supporting moral values are critical
for maintaining public trust and avoiding the
exploitation of personal information.
Resilient supply chain management raises urban
logistics ethical issues. (Entezami & Havaeji,
2023) articulate the intricate balance between
time-to-market performance and reducing
environmental risk for green drug supply chains.
Artificial intelligence-based logistic optimization
can soften this balance through route-optimized
delivery, greenhouse gas emission reduction, and
compliance with environmental regulations.
Ethical questions are raised when speed is
prioritized over sustainability and open AI
programming is required to balance both
intentions.
Another key difficulty with AI-based urban
decision-making is bias and fairness. AI systems
commonly rely on historical data, which can be
suggestive of current social inequalities. If these
systems are not adequately developed, they may
produce biased results in sectors such as housing,
public services, and law enforcement (Yigitcanlar
& Cugurullo, 2020). To avoid these issues, cities
must create transparent AI algorithms and fairness
checks that ensure all residents are treated fairly
(Cugurullo, 2020).
AI can also help cities adapt to climate change by
directing the creation of resilient infrastructure.
Innovative technologies, such as amphibious
house, offer an ongoing approach to flood risk
mitigation (Naseri et al., 2024). These buildings
float on rising water levels which is keeping
people safe from flooding. Artificial intelligence
contributes to this effort by evaluating
environmental data to forecast flooding patterns
and assist the construction of such structures.
When AI and sustainable design are linked, urban
resilience increases while disaster risk decreases.
Job loss and workforce changes are growing
concerns as AI automates urban services.
Although AI increases efficiency it may also
replace workers in sectors like transportation
administration and customer support (Mrabet &
Sliti, 2024). Cities need to create training and
education programs to help workers shift to new
jobs in an AI-driven economy (Khan et al., 2023).
The digital gap exacerbates inequalities in smart
city services. AI-powered systems rely on digital
infrastructure, which is not necessarily accessible
to everyone. Underserved populations may
struggle to obtain basic services such as
healthcare, education, and transportation (Bibri et
al., 2023). Without inclusive policies, these
people may fall behind. Governments and city
planners must close this gap by increasing internet
access and ensuring that AI technologies serve all
residents equitably (Rieder et al., 2022).
Table 2: Challenges of AI Implementation
Challenge
Description
Data Privacy
Risk of unauthorized access to sensitive information
Ethical Concerns
AI bias and its impact on decision-making
High Costs
Expensive implementation and maintenance
Workforce Displacement
Automation replacing traditional jobs
Addressing these challenges requires a balanced
approach that integrates ethical considerations,
policy regulations, and technological
advancements. Without proactive measures, AI-
driven smart cities risk deepening social
inequalities rather than fostering sustainable and
inclusive urban environments.
4. Future Trends and Innovations
C0 5 (2), 1136-1147 (2025)
1142
Amin Golkarfard et al.
The future smart city will be shaped by current
advancements in AI and its integration with other
emerging technologies. Several important trends
and innovations will greatly affect urban
development sustainability and efficiency.
AI-driven digital twins are increasingly being
used in urban planning. The virtual
representations emulate physical urban
infrastructure with real-time information from IoT
sensors and AI modeling. Digital twins help city
planners model various scenarios and predict
outcomes in terms of infrastructure sustainability
and disaster response (Xu et al., 2024). Cities can
streamline resource planning, traffic management,
and resilience to climate change through the use
of such models (Alnaser et al., 2024).
Interactive design-of-experiments (DoE) in
artificial intelligence (AI) optimizes complex
urban systems by optimizing cooling efficiency in
smart buildings. By capturing various design
parameters, the AI-driven DoE technique
optimizes system versatility and energy efficiency
in real-world implementation, thus contributing to
sustainable smart city infrastructure (Splechtna et
al., 2024).
Digital marketing is transforming citizen
engagement in smart cities with data-driven
approaches. (Saremi et al., 2024) propose a
framework under which customer engagement and
knowledge management enhance marketing
effectiveness. This approach allows municipalities
to provide personalized urban services, improve
communication, and increase public engagement.
Integration of digital marketing with smart city
initiatives guarantees that the development of
technology keeps pace with citizens' expectations,
with urban governance becoming responsive and
effective.
AI is transforming financial operations in
intelligent cities by integrating digital
technologies that enhance fraud detection, credit
scoring, and automated asset management. The
transformation optimizes financial service
delivery, increases efficiency, and enhances
financial infrastructure resilience (Pazouki,
Jamshidi, et al., 2025).
Generative AI is also proving to be a
revolutionary instrument in the creation of smart
grid communication. Distributed learning
frameworks facilitated by generative AI can help
improve data transmission, fault detection, and
energy efficiency in smart grids, as noted by
(Sajjadi Mohammadabadi, Entezami, et al., 2024).
With this technology, cities can reduce power
outages, improve grid resilience, and optimize
energy usage, thus making AI-driven grids an
extremely critical component of sustainable urban
infrastructure in the future.
Federated learning (FL) is transforming smart city
data sharing with accelerated decentralized
learning in heterogeneous environments. Dynamic
tiering improves communication efficiency and
reduces training latencies in smart city IoT
networks. This enables the easier integration of
autonomous devices, real-time data analysis, and
faster decision-making without compromising
privacy (Mahmoud Sajjadi Mohammadabadi et
al., 2025). By reducing latency, FL speeds up
resilience and responsiveness in city services,
from transport to energy management.
Decentralized management of energy is emerging
as one of the new trends in urban sustainability.
(Hashemi et al., 2016) suggest strong model
predictive control (MPC) for energy hubs that
maximizes use of renewable and conventional
energy sources in smart cities. By decentralizing
control systems, city planners will be able to have
more resilient, dynamic, and energy-efficient
infrastructure, which is needed to maintain
stability in the energy supply and lower carbon
emissions during peak demand.
AI is also being combined with IoT blockchain
and 5G networks to create more integrated city
ecosystems. IoT sensors collect and transmit
massive amounts of data which is analyzed by AI
to optimize city services such as waste
management energy supply and security
surveillance (Khan et al., 2023). Blockchain
technology ensures data integrity and transparency
while 5G ensures faster and more reliable
communication between smart city components
(Bibri et al., 2023). These technologies are also
facilitating more intelligent and responsive urban
systems through their integration (Babu et al.,
2023).
Silicon Valley's technological advancements,
inspired by venture capital investment, have
accelerated innovations in AI technologies that
strengthen world financial markets. Such
innovations constitute the financial leg of smart
C0 5 (2), 1136-1147 (2025)
1143
Amin Golkarfard et al.
cities by enhancing streams of investment,
enabling AI-based wealth management, and
increasing urban economic forecasting
(Ahmadirad, 2024b). Through sustainable
innovation systems, city planners can offer long-
term financial growth, improved urban living, and
an enhanced digital economy.
With the development of technology of AI, its
application in cultural and city beauty is being
expanded. Generative AI and machine learning
processes inspired by Zen art and visual images
have opened a new path for city planning. (Panahi
et al., 2018) describe cities can recreate natural
artistic process to create a harmonious city view
that combines ancient beauty with modern
functionality. With the integration of aesthetic
algorithms with urban planning, AI transforms
cities into more vibrant, good-looking and people
friendly places.
Satellite synchronization based on artificial
intelligence (AI)-based control systems offers new
avenues for city surveillance and disaster
mitigation. The recent development of integral
terminal sliding mode controllers provides the
capability to stabilize chaotic leader-follower
satellites' angular velocities to enable precise city
air quality, water system, and traffic surveillance
(Azadmanesh et al., 2024). The AI-based
synchronization approach enhances the accuracy
of real-time information, which is essential for
smart city sustainable urban planning and crisis
management.
The use of AI to revolutionize city planning and
design extends beyond the mere automation of
processes to more innovative applications like
predictive analytics and real-time resource
management. (Naseri, 2024a) believes that AI can
crunch massive amounts of data gleaned from
sensors embedded in urban infrastructure to allow
cities to manage energy consumption, regulate
mobility, and optimize waste management in real
time. Apart from improving urban operations'
output, artificial intelligence technologies help to
lower the whole environmental effect of cities.
Urban designers may create a more transparent
and sustainable urban environment by means of
the integration of artificial intelligence and newly
developed technologies such the Internet of
Things (IoT) and blockchain (Naseri, 2024a).
Table 3: Future Trends in AI for Smart Cities
Trend
Impact
Digital Twins
Virtual city models for better planning
AI in Governance
Data-driven decision-making
AI-Blockchain Integration
Secure and transparent data management
AI-enabled mobility solutions are transforming
transport in smart cities. Intelligent public
transport autonomous cars and AI-driven traffic
management are increasing efficiency and
reducing congestion (Yigitcanlar & Cugurullo,
2020). Predictive analysis and AI-driven traffic
signals are optimizing urban mobility by reducing
journey time and minimizing carbon emissions
(Bibri et al., 2023). AI-enabled smart public
transport systems introduce passenger demand
management improve scheduling and increase
commuter satisfaction (Cugurullo, 2020).
Another groundbreaking use is the application of
generative AI in urban modeling and design.
Generative AI uses machine learning to create the
most optimized building layouts urban areas and
green infrastructure alternatives (Rieder et al.,
2022). Real-time data can be used by AI to offer
adaptive urban design that maximizes efficiency
environmental sustainability and habitability
(Naseri, 2024b). These AI-designed models can
be utilized by architects and planners to scan
through various design options and choose the
most promising ones for use in future
developments (Bibri et al., 2023).
Transport equity is increasing in importance as
cities become more intelligent. A general model
developed by (Halimi et al., 2025) quantifies
transport equity based on disparities in urban
ground transport access. AI can trace
underprivileged areas and propose fair public
transportation expansions, where poor
neighborhoods have better access to city services.
The innovation does not merely encourage
mobility but also transportation planning
inclusivity.
C0 5 (2), 1136-1147 (2025)
1144
Amin Golkarfard et al.
Cities have to embrace adaptive governance
systems and legislative policies to guarantee
ethical implementation of such AI-driven trends
rising in them. The effectiveness of embedding
these technologies into current urban surroundings
while resolving any hazards and guaranteeing
inclusion will determine the direction of smart
cities. By means of efficient use of artificial
intelligence, smart cities may achieve equilibrium
between technological progress and sustainable
development, thereby fostering effective resilient
and inclusive cities for all.
2. Conclusion
The future success of smart cities depends on how
much artificial intelligence is integrated into cities
to promote efficiency sustainability and livability.
AI can transform cities by optimizing
transportation improving energy usage and
responsiveness of public services. However, cities
must address the ethical issues that come with AI
like data privacy job displacement and the digital
divide. To create truly intelligent and inclusive
cities policymakers urban planners and tech
developers must work together so that AI benefits
all citizens equally. The future requires
responsible AI governance investment in digital
infrastructure and continued innovation to align
technological progress with social responsibility.
Since cities continue to evolve AI will continue to
be a leading driving force of change shaping
urban areas that are more adaptive resilient and
sustainable for future generations.
Bibliography
1. Ahmadirad, Z. (2024a). Evaluating the
Influence of AI on Market Values in Finance:
Distinguishing Between Authentic Growth
and Speculative Hype. International Journal
of Advanced Research in Humanities and Law,
1(2),50–57. https://doi.org/10.63053/IJREL.1
1
2. Ahmadirad, Z. (2024b). The Beneficial Role
of Silicon Valley’s Technological Innovations
and Venture Capital in Strengthening Global
Financial Markets. International Journal of
Modern Achievement in Science, Engineering
and Technology, 1(3), 9–17. https://doi.org/10
.63053/IJSET.40
3. Alnaser, A. A., Maxi, M., & Elmousalami, H.
(2024). AI-Powered Digital Twins and
Internet of Things for Smart Cities and
Sustainable Building Environment. Applied
Sciences 2024, Vol. 14, Page 12056, 14(24),
12056. https://doi.org/10.3390/APP142412056
4. Azadmanesh, M., Roshanian, J., Georgiev, K.,
Todrov, M., & Hassanalian, M. (2024).
Synchronization of angular velocities of
chaotic leader-follower satellites using a novel
integral terminal sliding mode controller.
Aerospace Science and Technology, 150,
109211. https://doi.org/10.1016/J.AST.2024.1
09211
5. Babu, V. V., Preetha Roselyn, J., &
Sundaravadivel, P. (2023). Multi-objective
genetic algorithm based energy management
system considering optimal utilization of grid
and degradation of battery storage in
microgrid. Energy Reports, 9, 5992–6005.
https://doi.org/10.1016/J.EGYR.2023.05.067
6. Balcı, O., Almohammad, D., Kılıç, C., &
Singh, R. (2025). Harnessing Emerging
Technologies for Green Sustainability:
Innovations, Challenges, and Future Pathways.
Climate Change Management, Part F106,
209–225. https://doi.org/10.1007/978-3-031-8
32 50-5_13
7. Barati-Nia, A., Earl Parrott, A., Sorenson, K.,
Moug, D. M., & Khosravifar, A. (2025).
Comparing Cyclic Direct Simple Shear
Behavior of Fine-Grained Soil Prepared with
SHANSEP or Recompression Approaches.
Geotechnical Frontiers 2025, 419–429. https
://doi.org/10.1061/9780784485996.040
8. Bibri, S. E., Alexandre, A., Sharifi, A., &
Krogstie, J. (2023). Environmentally
sustainable smart cities and their converging
AI, IoT, and big data technologies and
solutions: an integrated approach to an
extensive literature review. Energy
Informatics 2023 6:1, 6(1), 1–39. https://doi.
org/10.1186/S42162-023-00259-2
9. Cugurullo, F. (2020). Urban Artificial
Intelligence: From Automation to Autonomy
in the Smart City. Frontiers in Sustainable
Cities, 2, 494301. https://doi.org/10.3389/FRS
C.2020.00038/BIBTEX
10. Entezami, M., Basirat, S., Moghaddami, B.,
Bazmandeh, D., & Charkhian, D. (2025).
Examining the Importance of AI-Based
Criteria in the Development of the Digital
Economy: A Multi-Criteria Decision-Making
Approach. Journal of Soft Computing and
C0 5 (2), 1136-1147 (2025)
1145
Amin Golkarfard et al.
Decision Analytics, 3(1), 72–95. https://doi.
org/ 10.31181/JSCDA31202555
11. Entezami, M., & Havaeji, H. (2023). Green
Drug Supply Chain Investigation by Time-
Market Balance and Risk. World Journal of
Engineering and Technology, 11(03), 611–
631. https://doi.org/10.4236/WJET.2023.1130
42
12. EskandariNasab, M., Hamdi, S. M., &
Boubrahimi, S. F. (2024). ChronoGAN:
Supervised and Embedded Generative
Adversarial Networks for Time Series
Generation. https://arxiv.org/abs/2409.14013
v1
13. Espahbod, S. (2020). Intelligent Freight
Transportation and Supply Chain Drivers: A
Literature Survey. Uncertainty and Operations
Research, 49–56. https://doi.org/10.1007/978-
981-15-5720-0_6
14. Espahbod, S. (2024). Market Information,
Human Behaviour, and Policy: Dissecting the
Trucking Freight Spot Markets in Three
Essays. Theses and Dissertations
(Comprehensive). https://scholars.wlu.ca/etd/
2606
15. Halimi, Z., SafariTaherkhani, M., & Cui, Q.
(2025). A Generalized Framework for
Assessing Equity in Ground Transportation
Infrastructure: Exploratory Study. Journal of
Management in Engineering, 41(2). https://doi
.org/10.1061/JMENEA.MEENG-6336
16. Hashemi, Z., Ramezani, A., & Moghaddam,
M. P. (2016). Energy hub management by
using decentralized robust model predictive
control. 2016 4th International Conference on
Control, Instrumentation, and Automation,
ICCIA 2016, 105–110. https://doi.org/10.1
109/ICCIAUTOM.2016.7483144
17. Haughton, M., Rostami, B., & Espahbod, S.
(2022). The price of anarchy in truckload
transportation spot markets. EURO Journal on
Transportation and Logistics, 11, 100071.
https://doi.org/10.1016/J.EJTL.2022.100071
18. Herath, H. M. K. K. M. B., & Mittal, M.
(2022). Adoption of artificial intelligence in
smart cities: A comprehensive review.
International Journal of Information
Management Data Insights, 2(1), 100076.
https://doi.org/10.1016/J.JJIMEI.2022.100076
19. Karkehabadi, A., & Sadeghmalakabadi, S.
(2024). Evaluating Deep Learning Models for
Architectural Image Classification: A Case
Study on the UC Davis Campus. 2024 IEEE
8th International Conference on Information
and Communication Technology (CICT), 1–6.
https://doi.org/10.1109/CICT64037.2024.1089
9686
20. Khan, M. A., Saleh, A. M., Waseem, M., &
Sajjad, I. A. (2023). Artificial Intelligence
Enabled Demand Response: Prospects and
Challenges in Smart Grid Environment. IEEE
Access, 11, 1477–1505. https://doi.org/10.
1109/ACCESS.2022.3231444
21. Khatami, S. S., Shoeibi, M., Salehi, R., &
Kaveh, M. (2025). Energy-Efficient and
Secure Double RIS-Aided Wireless Sensor
Networks: A QoS-Aware Fuzzy Deep
Reinforcement Learning Approach. Journal of
Sensor and Actuator Networks 2025, Vol. 14,
Page 18, 14(1), 18. https://doi.org/10.3390/JS
AN14010018
22. Leal Filho, W., Mbah, M. F., Dinis, M. A. P.,
Trevisan, L. V., de Lange, D., Mishra, A.,
Rebelatto, B., Ben Hassen, T., & Aina, Y. A.
(2024). The role of artificial intelligence in the
implementation of the UN Sustainable
Development Goal 11: Fostering sustainable
cities and communities. Cities, 150, 105021.
https://doi.org/10.1016/J.CITIES.2024.105021
23. Mahmoud, S., & Mohammadabadi, S. (2025).
From Generative AI to Innovative AI: An
Evolutionary Roadmap. https://arxiv.org/abs/
2503.11419v1
24. Mahmoud Sajjadi Mohammadabadi, S.,
Zawad, S., Yan, F., & Yang, L. (2025). Speed
Up Federated Learning in Heterogeneous
Environments: A Dynamic Tiering Approach.
IEEE Internet of Things Journal, 12(5), 5026–
5035. https://doi.org/10.1109/JIOT.2024.348
7473
25. Mrabet, M., & Sliti, M. (2024). Integrating
machine learning for the sustainable
development of smart cities. Frontiers in
Sustainable Cities, 6, 1449404. https://doi.org/
10.3389/FRSC.2024.1449404/BIBTEX
26. Muralidhara Rao, P., Al-Turjman, F., &
Deebak, B. D. (2022). Smart grid metering:
security, privacy, and open challenges.
Sustainable Networks in Smart Grid, 255–272.
https://doi.org/10.1016/B978-0-323-85626-
3.00009-0
27. NASA’s Scientific Visualization Studio.
(2025). Global Temperature | Vital Signs –
Climate Change: Vital Signs of the Planet.
C0 5 (2), 1136-1147 (2025)
1146
Amin Golkarfard et al.
https://climate.nasa.gov/vital-signs/global-
temperature/?intent=121
28. Naseri, S. (2024a). AI in Architecture and
Urban Design and Planning: Case studies on
three AI applications. Https:// Gsconlin epress
.Com/, 21(02), 565–577. https://doi.org/ https:
//doi.org/10.30574/gscarr.2024.21.2.0463
29. Naseri, S. (2024b). AI in Architecture and
Urban Design and Planning: Case studies on
three AI applications. Https://Gsconlinepress.
Com/Journals/Gscarr/Sites/Default/Files/GSC
ARR-2024-0463.Pdf, 21(2), 565–577. https://
doi.org/10.30574/GSCARR.2024.21.2.0463
30. Naseri, S., Eshraghi, S., & Talebian, | Shima.
(2024). “Innovative Sustainable Architecture
A Lesson Learned from Amphibious House in
the UK". Current Opinion, 4(4), 766–777.
https://doi.org/10.52845/CURRENTOPINION
.V4I4.318
31. Oskooie, S., Civil, S. N.-J. of S. A. and, &
2023, undefined. (2023). An Assessment of
the Walkability of Square Corridors in
Developing Countries: a Case Study of
Saadatabad, Tehran, Iran. Ceeol.ComSFE
Oskooie, S NorouziJournal of Sustainable
Architecture and Civil Engineering, 2023
•ceeol.Com, 1(32), 77–91. https://doi.org/ 10.
5755/j01.sace.32.1.31992
32. Panahi, S., Cheraghifar, F., & Talebian, S.
(2018). An Investigation on Painting and
Imagery in Zen. Modern Applied Science,
12(9), 200. https://doi.org/10.5539/MAS.V12
N9P200
33. Pazouki, S., Behdad, M. (, Jamshidi, ), Jalali,
M., & Tafreshi, A. (2025). Transformative
Impact of AI and Digital Technologies on the
FinTech Industry: A Comprehensive Review.
International Journal of Advanced Research
in Humanities and Law, 2(2), 1–27. https://
doi.org/10.63053/IJREL.37
34. Pazouki, S., Jamshidi, M. (Behdad), Jalali, M.,
& Tafreshi, A. (2025). Artificial Intelligence
and Digital Technologies in Finance: A
Comprehensive Review. Journal of
Economics, Finance and Accounting Studies ,
7(2), 54–69. https://doi.org/10.32996/JEFAS.
2025.7.2.5
35. Pazouki, S., Metamax, M. B. J., Jalali, S. M.,
Tafreshi, A., Bank, M., & York, N. (2025).
The Integration of Big Data in finTech:
Review of Enhancing Financial Services
Through Advanced Technologies. World
Journal of Advanced Research and Reviews,
25(1), 546–556. https://doi.org/10.30574/wja
rr.2025.25.1.0060
36. Rieder, E., Schmuck, M., & Tugui, A. (2022).
A Scientific Perspective on Using Artificial
Intelligence in Sustainable Urban
Development. Big Data and Cognitive
Computing 2023, Vol. 7, Page 3, 7(1), 3. https:
//doi.org/10.3390/BDCC7010003
37. Roya Nazari Najafabadi, Sana Avar,
Mohammad Karimi, Mahsa Anbari, & Sajjad
Naseri. (2024). Ecological restoration of
historical monuments (With a Focus on the
Restoration of Chogha Zanbil). World Journal
of Advanced Research and Reviews, 23(2),
240–250. https://doi.org/10.30574/WJARR.20
24.23.2.2303
38. Sajjadi Mohammadabadi, S. M., Entezami,
M., Karimi Moghaddam, A., Orangian, M., &
Nejadshamsi, S. (2024). Generative artificial
intelligence for distributed learning to enhance
smart grid communication. International
Journal of Intelligent Networks, 5, 267–274.
https://doi.org/10.1016/J.IJIN.2024.05.007
39. Sajjadi Mohammadabadi, S. M., Yang, L.,
Yan, F., & Zhang, J. (2024). Communication-
Efficient Training Workload Balancing for
Decentralized Multi-Agent Learning.
Proceedings - International Conference on
Distributed Computing Systems, 680–691.
https://doi.org/10.1109/ICDCS60910.2024.00
069
40. Saremi, Y., Aghaei, M., Esfid, M. R.,
Hasangholipour, T., & Ansari, M. (2024).
Providing Digital Marketing Model with
Customer Participation Approach with
intervening role of knowledge management.
International Journal of Knowledge
Processing Studies, 4(2), 148–167. https://doi.
org/10.22034/KPS.2023.410230.1154
41. Saremi, Y., Yalda, S., 1+, S., & Taghizadeh,
M. (2013). RFID Adoption by Supply Chain
Organizations in Malaysia. International
Proceedings of Economics Development &
Research, 59. https://doi.org/10.7763/IPEDR
42. Splechtna, R., Behravan, M., Jelovic, M.,
Gracanin, D., Hauser, H., & Matkovic, K.
(2024). Interactive Design-of-Experiments:
Optimizing a Cooling System. IEEE
Transactions on Visualization and Computer
Graphics. https://doi.org/10.1109/TVCG.20
24.3456356
C0 5 (2), 1136-1147 (2025)
1147
Amin Golkarfard et al.
43. Talebian, S., Golkarieh, A., Eshraghi, S.,
Naseri, M., Naseri, S., & Naseri, S. (2025).
Artificial Intelligence Impacts on Architecture
and Smart Built Environments: A
Comprehensive Review. Aceesjr.Com,
2024(1), 45–56. https://doi.org/10.22034/ ace
es.2025.488106.1013
44. Wang, W., Li, L., Shi, V., & Espahbod, S.
(2024). Carbon Emission Accounting and
Reduction for Buildings Based on a Life Cycle
Assessment: A Case Study in China’s Hot-
Summer and Warm-Winter Region.
Sustainability 2024, Vol. 16, Page 5842, 16
(14), 5842. https://doi.org/10.3390/SU161458
42
45. Xu, H., Omitaomu, F., Sabri, S., Zlatanova, S.,
Li, X., & Song, Y. (2024). Leveraging
generative AI for urban digital twins: a
scoping review on the autonomous generation
of urban data, scenarios, designs, and 3D city
models for smart city advancement. Urban
Informatics 2024 3:1, 3(1), 1–44. https://doi.
org/10.1007/S44212-024-00060-W
46. Yigitcanlar, T., & Cugurullo, F. (2020). The
Sustainability of Artificial Intelligence: An
Urbanistic Viewpoint from the Lens of Smart
and Sustainable Cities. Sustainability 2020,
Vol. 12, Page 8548, 12(20), 8548. https://doi.
org/10.3390/SU12208548
47. Zafar, S. (2024). Smart Cities: The Role of AI
in Urban Planning and Sustainable
Development. Artificial Intelligence
Multidisciplinary Journal of Systems and
Applications, 1(1), 27–39. https://www.rese
archcorridor.org/index.php/aimjsa/article/view
/266
