The global transition towards renewable energy lacks intelligent frameworks for solar plant locations optimization and long-term energy generation forecasting. This research presents a comprehensive AI based methodology for identifying optimal SOLAR plant installation sites and forecasting energy output for a period of 10-year. The proposed methodology integrates satellite-derived meteorological data from NASA POWER database, photovoltaic performance modelling, multi-parameter feature engineering, Random Forest regression, and suitability-based spatial ranking. Annual energy generation is computed in MWh using physical PV system parameters including panel area, efficiency, and performance ratio. A generalized and geographically adaptable framework is developed to enable scalable renewable energy planning. The initial Experimentation projects high predictive accuracy (R² ? 0.91) and low error margins, validating the effectiveness of the approach. The framework supports strategic energy infrastructure planning, investment decision-making, and sustainable policy development.