Renewable energy sources, solar panels or wind turbines produce large volumes of electronic waste (e-waste) when their lifecycle ends. Incorrect recycling of this e-waste results to severe environmental and economic issues brought about by toxic materials and unproductive recycling techniques. The e-waste management process in the renewable energy sector is now disunified, unfocused, and uncoordinated without data exchange in real-time. These constraints form obstacles to the optimization and sustainability of recycling supply chain. This paper fills these gaps by introducing a smart supply chain model that is composed using as much as possible optimization algorithms and artificial intelligence (AI) functions to ameliorate solar panels and wind turbines e-waste recycling. The primary implication of this study is introducing a detailed, data-based framework that facilitates the principles of circulareconomy by implementing closed-loop material cycles and by seeing as much as possible of the resource back to circulation. The mix of qualitative supply chain mapping with quantitative mathematical optimization as a mixed-method is solved using metaheuristic algorithms. Sensors based on IoT and AI-based sorting enhance data collection and decision-making, and vehicle routing plus inventory management is optimized to minimize cost and scale the environmental harm. Findings indicate a 75% recycling efficiency, 30% increase in collection levels, 15% decrease in cost of operations and a 20% loss in CO2 emissions over pre-conditions. Such results indicate the applicability of the model in complementing sustainable and cost friendly recycling of e-waste within the renewable energy industry.