While successful integration of solar energy into the grid is now emerging, there should be accurate regression from solar power generation to radiation levels. From this work came the development of “Solar Intelligence,” a system employing immediately deployable machine-learning-based predictive models. These models would be trained on an extensive array of data sources, including historical solar radiation measurements, weather forecasts, and environmental factors. Analysis of these complicated relationships in Solar Intelligence aims to predict the next generation of solar power and radiation accurately. Such fine forecasts would enable grid operators to optimize energy production, smoothly combine renewable sources, and foster overall grid stability. The “Solar Intelligence” system offers both utilities and private users the potential to transform solar energy management through informed decision-making and maximum utilization of clean, sustainable energy.