The exponential growth of global e-commerce has significantly increased the volume of returns and reverse logistics, posing challenges to supply chain efficiency and sustainability. Traditional return management processes are often resource-intensive, costly, and environmentally unsustainable. Integrating Artificial Intelligence (AI) into returns and reverse logistics optimization presents transformative opportunities to streamline operations, minimize waste, and enhance sustainability. This research explores the role of AI-driven technologies such as machine learning, predictive analytics, and IoT in optimizing reverse logistics processes. Key areas of focus include automated returns processing, smart inventory management, demand forecasting, and sustainable resource utilization. Through case studies and quantitative analysis, the study demonstrates how AI can significantly reduce carbon footprints, improve cost-efficiency, and support circular economy principles in supply chain management (SCM). The findings highlight AI's potential to revolutionize reverse logistics, making it a cornerstone of sustainable development in modern SCM practices.