The global transition toward renewable energy is driven by concerns over climate change, energy security, and the finite nature of fossil fuels. While renewable sources such as solar, wind, hydro, and geothermal provide environmental and economic advantages, their inherent intermittency poses significant challenges for integration into existing power systems. Unlike conventional fossil fuel-based generation, renewable energy output is highly variable, complicating energy forecasting, storage, distribution, and grid stability. Artificial Intelligence (AI) offers promising solutions to these challenges by enabling data-driven optimization of energy systems. Techniques including machine learning, deep learning, and reinforcement learning facilitate advanced predictive modeling, real-time decision-making, and intelligent grid management. These AI-driven approaches improve energy forecasting accuracy, optimize operational efficiency, and enhance predictive maintenance, thereby increasing the reliability and sustainability of renewable energy systems. This study highlights the emerging methods and applications of AI in overcoming renewable energy integration challenges, providing a pathway toward more efficient, resilient, and environmentally sustainable energy infrastructures.