The increasing share of variable renewable energy sources in the power grid has brought about tremendous challenges in the context of stability and reliability. An active energy stor-age management system is designed and presented in this paper to cater to the intermitten-cy of renewable resources while keeping the grid stable. The study develops and validates a novel hybrid energy storage management system that combines battery and supercapacitor technologies with machine learning optimization algorithms. The research methodology em-ploys a dual-layer control architecture integrating reinforcement learning for strategic energy dispatch and model predictive control for real-time operation. System performance was eval-uated using a comprehensive testbed comprising a 500kW solar installation, 250kWh battery storage, and 50kW supercapacitor array across varying weather and load conditions over six months. The system proposed, yielded results that were 27% better in overall energy perfor-mance than traditional storage management approaches while reducing voltage fluctuations by 43%. The machine learning algorithm successfully predicted renewable generation patterns with 92% accuracy, enabling proactive storage management strategies that reduced peak de-mand charges by 31%. The system maintained consistent performance across seasonal varia-tions, with high availability (99.97%) and significant reductions in maintenance requirements (62.5% fewer events). The successful integration of hybrid storage technologies with advanced machine learning algorithms establishes a viable framework for enhancing grid stability and economic performance in renewable-rich microgrids. The results reveal meaningful aspects for developing next-gen smart grid storage solutions for applications, particularly where com-paratively high reliability is needed to integrate renewables efficiently.