Abstract: As urbanization accelerates and populations continue to grow, the volume of solid waste is soaring and traditional management approaches are increasingly constrained by low efficiency, high costs and significant environmental risks. Artificial intelligence (AI), with its exceptional abilities in data analytics, pattern recognition and intelligent decision-making, offers a transformative opportunity to modernize waste management systems. This paper evaluates the strengths, limitations and applicable scenes of mainstream AI algorithms, including artificial neural networks, support vector machines, decision trees, genetic algorithms and linear regression. It also provides a systematic review of AI′s advances across critical verticals of integrated waste governance, such as collection, sorting, disposal, resource recovery and illegal dumping detection. To tackle current challenges such as inconsistent data quality, opaque "black-box" models, privacy and security concerns, infrastructure compatibility, we propose strategies encompassing data standardization, the introduction of explainability tools, privacy safeguards and edge-to-cloud collaboration. Finally, we offer an in-depth outlook on the deep integration of AI with Internt of Things (IoT), digital twins and blockchain, aiming to inform future efforts toward greener, smarter and more sustainable waste management.