<![CDATA[The accelerating global transition toward decarbonized power systems has positioned renewable energy—particularly solar and wind as the backbone of future electricity supply, yet the inherent variability of these resources continues to challenge grid reliability and economic viability. Artificial intelligence (AI) has emerged as a decisive enabler for overcoming this variability by improving forecasting precision, enabling adaptive control, and supporting predictive maintenance across renewable energy assets. This article synthesizes findings from twenty-five peer-reviewed studies published between 2021 and 2026 to examine how machine learning, deep learning, reinforcement learning, and metaheuristic algorithms are optimizing renewable energy systems. A structured literature review methodology was applied, covering identification, screening, eligibility, and thematic synthesis stages. The findings indicate that deep learning architectures such as long short-term memory networks and convolutional neural networks substantially reduce forecasting error, while reinforcement learning and IoT-integrated frameworks improve real-time dispatch and resource allocation. Predictive maintenance applications report downtime reductions of up to twenty-five percent and inspection-time savings approaching ninety percent. The review further identifies a genuine novelty: existing studies remain fragmented across isolated technical domains, whereas an integrated, system-level framework linking forecasting, control, and maintenance intelligence is still underdeveloped. The article concludes by outlining research directions toward standardized datasets, explainable models, and federated architectures for scalable AI-enabled renewable energy optimization.]]>
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