With escalating global energy demands, sustainable solutions for renewable energy integration are increasingly essential. Traditional grids struggle to accommodate the variable nature of renewable energy sources, leading to inefficiencies and reliability challenges. This research proposes an AI-driven framework designed specifically to optimize the integration of renewable energy in smart grids, enhancing both stability and efficiency. Leveraging advanced Machine Learning and Reinforcement Learning algorithms, the framework addresses the complexities of real-time renewable energy management, making adaptive, data-driven adjustments that improve grid responsiveness to fluctuating renewable energy inputs. Our approach utilizes vast datasets from solar and wind energy sources, enabling Machine Learning models to accurately forecast generation patterns. Building on these predictions, Reinforcement Learning adjusts grid parameters dynamically to align renewable supply with consumption demands, reducing dependency on non-renewable sources and minimizing energy losses. This pioneering framework represents a significant advancement in renewable energy management for smart grids, contributing to a resilient and sustainable energy infrastructure.