The chapter explores the transformative potential of artificial intelligence (AI) in enhancing the efficiency and sustainability of solid waste management (SWM) systems. As urbanization and population growth escalate waste generation, traditional waste management approaches are increasingly inadequate. This chapter examines the integration of AI technologies, such as machine learning, computer vision, and IoT, to optimize various SWM processes, including waste collection, sorting, recycling, and disposal. The objective of the chapter is to assess the current applications and prospects of AI tools in solid waste management in the global context. This study assesses current technologies, their performance across waste collection, sorting, recycling, and disposal, and future opportunities for advancement. The chapter objectives are interlinked to provide a structured exploration, beginning with a conceptual foundation of SWM challenges and AI's role in addressing them. The chapter begins by discussing the challenges faced in solid waste management, such as inefficient waste segregation, high operational costs, and environmental impacts. It then delves into how AI tools can address these issues by automating tasks, predicting waste generation patterns, and improving resource recovery rates. Case studies highlight successful implementations of AI-driven solutions in smart waste bins, robotic sorters, and route optimization for waste collection vehicles, demonstrating significant cost savings and reduced carbon footprints. Furthermore, the chapter addresses the barriers to widespread adoption of AI in SWM, including data privacy concerns, lack of infrastructure, and the need for skilled personnel. It concludes with a discussion on future prospects, emphasizing the role of AI in enabling circular economy practices and promoting sustainable urban development. This comprehensive assessment underscores the critical role of AI in revolutionizing solid waste management, paving the way for greener and more resilient cities.