The global transition toward sustainable energy systems is accelerating due to increasing concerns about climate change, carbon emissions and energy security. Renewable energy sources such as solar and wind provide environmentally sustainable alternatives to fossil fuels; however, their inherent intermittency, stochastic behavior and environmental dependency introduce significant operational and planning challenges for modern energy systems. Artificial intelligence (AI) and deep learning techniques have recently emerged as effective solutions for addressing these challenges through accurate forecasting, intelligent control and optimized energy management. This review paper presents a systematic and comprehensive analysis of recent advancements in AI-driven approaches for renewable energy assessment and management. A structured literature review methodology was adopted to analyze 30 peer-reviewed research articles obtained from major scientific databases, including IEEE Xplore, ScienceDirect and Google Scholar. The selected studies were categorized into four major application domains: solar energy forecasting, wind energy optimization, hydropower resource management and hybrid renewable energy systems. The review critically evaluates widely adopted machine learning and deep learning models such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and hybrid AI frameworks, highlighting their predictive capabilities and operational benefits. Furthermore, this study identifies key research gaps related to data availability, model interpretability and scalability while outlining future research directions for developing intelligent, reliable, and sustainable renewable energy systems.