The growing transition to renewable energy sources, such as solar, wind, hydro, and geothermal, presents significant challenges due to their intermittent nature, requiring advanced optimization strategies for efficient integration into existing power systems. Artificial Intelligence (AI) has emerged as a transformative tool in addressing these challenges by enhancing energy forecasting, optimizing smart grid operations, and improving predictive maintenance. This study employs a secondary research methodology, conducting a qualitative analysis of existing literature, case studies, and technical reports on AI applications in renewable energy optimization. The study explores how machine learning (ML), deep learning (DL), and reinforcement learning (RL) enhance energy forecasting, real-time grid management, and fault detection. AI-driven models improve the accuracy of solar and wind energy predictions, allowing better supply-demand balancing. Additionally, AI-powered smart grids enhance energy distribution, reduce wastage, and increase grid reliability. AI also enables predictive maintenance, minimizing operational costs and infrastructure failures. The research is grounded in Systems Theory, Optimization Theory, and Technological Determinism, offering a structured framework for analyzing AI’s impact on renewable energy systems. Despite its advantages, AI integration faces challenges such as high implementation costs, data privacy concerns, and infrastructure compatibility issues. By reviewing real-world case studies, the study demonstrates AI’s effectiveness in optimizing renewable energy systems, emphasizing its potential to accelerate the transition toward a more sustainable and resilient energy future.