Bangladesh is facing increasing environmental and energy challenges. It's grows economically and urbanizes rapidly. This study explores how environmental and climate related factors influence the use of renewable energy across different districts in the country. By analyzing district-level data on temperature, rainfall, air quality, forest cover, urban growth, and carbon emissions, the research aims to build a predictive model to estimate renewable energy usage. An artificial intelligence framework was developed to predict renewable energy adoption through different regression models including Linear Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, and XGBoost. The Extra Trees Regressor was the most accurate and had the most consistent predictions using error metrics. In addition to standard performance measures (MSE, RMSE, MAE, R2), graphical examinations such as feature importance, residual plot, and actual vs. predicted plots were conducted for better understanding. The findings indicate the role of local environmental variables in shaping energy behaviors and can be utilized in evidence-based energy policy decisions. The research contributes to Bangladesh's sustainable development aspirations and transition toward cleaner, renewable energy.