This study evaluates the performance of a commercial silicon-based photovoltaic (PV) module under varying environmental conditions, including solar irradiance, module and ambient temperatures, humidity, and wind speed. Key performance indicators such as daily and lifetime energy output, CO2 reduction, and potential income were analyzed. Machine learning techniques, including Linear Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), and XGBoost, were employed to predict photovoltaic (PV) efficiency under varying environmental conditions. The results indicate that solar irradiance is the primary driver of energy production, while elevated temperatures and high humidity reduce efficiency, and wind speed provides minor cooling benefits. Among the models, XGBoost achieved the highest predictive accuracy (Test R2 = 0.9967), followed by RF and ANN, whereas LR underperformed due to a limited ability to capture nonlinear interactions. These findings highlight the critical influence of environmental and electrical factors on PV performance and demonstrate the effectiveness of advanced machine learning techniques, particularly XGBoost, in optimizing energy output and supporting sustainable energy planning.