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The rising priority placed on energy conservation, together with the adoption of renewable sources, has grown essential to sustainable practices across today’s university campuses. This research introduces an intelligent green energy management framework (SGEMS) that integrates machine learning predictions with reinforcement learning (RL) to enhance energy utilization and photovoltaic output on an eco-conscious campus. Leveraging historical records from three campus structures, the study developed a forecasting engine capable of projecting near-term electricity demand and solar yield. When paired with RL, the XGBoost method achieved markedly better accuracy in estimating consumption and production levels than competing algorithms, with root mean square errors (RMSEs) of 14.72, mean absolute errors (MAEs) of 12.00, and mean absolute percentage errors (MAPE) of 2.18%. The work also describes an accessible, browser-based dashboard that enables live energy oversight and supports timely decision-making, empowering facility managers to anticipate deficits and regulate consumption more intelligently. Overall, the presented methodology provides an adaptable blueprint that enables campuses to reduce dependence on utility grids, improve efficiency, and serve as a reference model for subsequent eco-friendly campus programs.