<![CDATA[General Background: The rapid global deployment of solar photovoltaic and wind energy systems is central to climate change mitigation but generates a growing end-of-life waste challenge. Specific Background: By 2050, cumulative waste from solar panels and wind turbine blades is projected to reach tens of millions of tons, while current linear recycling systems face technical inefficiencies and economic constraints. Knowledge Gap: There is a lack of scalable and economically viable circular management solutions capable of addressing complex composite materials and lifecycle optimization in renewable energy infrastructure. Aims: This study systematically evaluates Artificial Intelligence applications across the lifecycle of solar panels and wind turbines to assess their role in enabling circular economy strategies. Results: Based on a systematic review of 496 publications and quantitative synthesis, AI-driven solutions demonstrate 35.8% carbon emission reduction per recycled solar panel, 33% improvement in material recovery rates, 43.8% gains in disassembly efficiency, and 62.5 kg CO2 savings per logistics operation. Novelty: The study develops an integrated analytical framework linking Machine Learning, Computer Vision, Robotics, Digital Twins, and lifecycle assessment within renewable energy circularity. Implications: The findings support AI-enabled reverse logistics, Digital Product Passports, and policy-informed lifecycle management as foundational mechanisms for sustainable renewable energy systems. Keywords: Artificial Intelligence, Circular Economy, Renewable Energy Systems, Lifecycle Assessment, Waste Management Key Findings Highlights: Carbon savings of 35.8% achieved through intelligent recycling workflows Material recovery improvements reached up to 33% across PV components Logistics routing reduced transport-related CO2 by 62.5 kg per delivery]]>
