A supervised learning multi-variable linear regression model to assess the impact of replacing non-renewable energy sources with renewable energy on CO2 emissions in the UK was utilised in this paper. The model was developed and tested using various tools and libraries, including Scikit-learn, Jupyter Notebook, NumPy, SciPy, seaborn, and matplotlib. Data preprocessing and analysis were conducted using various methods. The analysis found that non-renewable energy sources have a positive impact on CO2 emissions, but substituting them with sustainable and green energy has an adverse effect. The model developed using selected features outperformed the model without feature selection, suggesting that the feature selection process improves performance and efficiency. The paper emphasises the need for further research and collaborative efforts to enhance the accuracy and reliability of AI predictive models for effective business policy formulation and decision-making.