The growing demand for sustainable energy solutions has highlighted the importance of utilizing solar power to address global energy needs while minimizing environmental impacts. This study focuses on predicting solar energy production in off-grid areas using scalable machine learning techniques. Historical solar energy generation and meteorological data are combined into a single dataset, and robust data preprocessing is performed to ensure high-quality analysis. Regression-based machine learning models, including the Linear Regression, Decision Trees, Random Forest, and k-Nearest Neighbors (kNN), are implemented and evaluated using performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results reveal that high-irradiance regions, exhibit strong potential for solar grid deployment, with accurate energy predictions supporting surplus energy harvesting. Conversely, areas with limited solar potential, are identified as better suited for hybrid or alternative energy systems. The study demonstrates the viability of machine learning-driven solar energy prediction to aid in energy planning and grid deployment, particularly in remote areas. These findings underscore the role of advanced analytics in promoting environmentally sustainable energy solutions and informed decision-making for renewable energy adoption.