Accumulation of damages during the service life of a structure can reduce its safety. Every structure that is constructed has a particular age but these structures can deteriorate before their service life due to various factors such as harsh environmental conditions, fatigue due to service loading, etc. To access the information regarding the health index of structure the need for various unconventional damage assessment practices and dependable structural health monitoring systems is presently high. Structures to perform efficiently damage assessment and appropriate retrofitting are required. Structural health monitoring (SHM) has been verified to be an economical technique for damage assessment in structures over the past several decades. In reinforced concrete beams flexural cracks distribute non-linearly and propagate along with all directions. The crack continues to propagate until the structure or structural component fractures. Due to this complex behavior of cracks, simplified damage simulation techniques such as reductions in the modulus of elasticity or section depth or stiffness of rotational spring elements cannot be applied to simulate flexural cracks in reinforced concrete components. Besides these simplified techniques, dynamic properties have been used extensively in the past. But this methodology has many disadvantages such as dynamic characteristics obtained through experiments can vary due to measurement errors, noise, and environmental changes, which can greatly affect precision. This research will address the above gap in knowledge by developing a model that can represent the complex behavior of cracks and then utilize artificial neural networks to assess damage in RC flexural members. In this study, consideration is given to the fundamental strategy for developing Plasticity Damage Models and ANNs to predict the extent and location of the damage from beam structures' measured detection data. ABAQUS finite element software is used with properly validated models of material throughout the study. A promising approach to evaluate and detect damage makes use of artificial neural networks (ANNs) to solve these two problems. ANNs are a strong artificial intelligence (AI) technique that has been widely accepted in predicting the extent and location of structural damage. ANNs are trained using deflection data obtained from intact and damaged beam structures simulations of finite elements. It is also found that reducing the number of required outputs significantly improved the quality of predictions made by ANN. The findings obtained were fair and they demonstrated strong alignment with the real values. This means that using ANNs is an excellent method for measuring damage and the issue of detecting damage. When using ANNs, some essential problems of traditional approaches to detecting damage can be solved and the precision of identification of damage can be improved.