The energy sector is undergoing a rapid transition toward renewable resources, such as solar and wind energy, with the aim of reducing dependence on fossil fuels and lowering carbon emissions. However, the intermittent nature of these energy sources presents significant challenges for maintaining the stability and efficiency of conventional power grids. This study investigates the integration of renewable energy sources into smart grids using intelligent control strategies and predictive models to enhance power distribution efficiency and environmental sustainability. A grid simulation model was developed based on real-world data, where artificial intelligence techniques were applied to predict energy generation and optimize load management. In addition, smart energy storage systems were incorporated to ensure power availability during peak demand periods and unexpected outages. The system’s performance was evaluated and compared with that of traditional power grids. The results indicate that integrating renewable energy with smart grid technologies reduces energy losses by up to 15% compared to conventional systems, while also improving voltage and frequency stability during peak loads and fluctuations. Furthermore, the proposed system achieves an approximately 12% reduction in carbon emissions and demonstrates strong capability in predicting renewable energy output, thereby supporting more effective energy planning. Overall, the findings highlight that the combination of intelligent control systems and renewable energy significantly improves grid efficiency, resilience, and sustainability. The study also provides practical solutions for managing renewable energy variability and reducing reliance on fossil fuels, thereby contributing to sustainable development goals. Future work is recommended to scale up the proposed system for application in large cities and national power grids, while further exploring advancements in energy storage and artificial intelligence to enhance grid performance and reliability.