Urban waste management is shifting from fixed, reactive collection toward data-driven and adaptive service models. This review synthesizes 33 recent studies and technical contributions on Artificial Intelligence of Things (AIoT) for smart waste management. The synthesis is organized around six analytical dimensions: IoT sensing and communication, real-time monitoring performance, dynamic routing, AI-based classification and robotic sorting, edge/fog/cloud intelligence, and circular governance. Unlike a descriptive survey, the paper develops an AIoT-SWM taxonomy and an evaluation rubric for comparing smart waste systems according to interoperability, latency, energy profile, scalability, decision autonomy, circular-economy contribution, and social inclusion. Reported deployments show measurable operational gains, including 26-35% reductions in fuel consumption, more than 30% improvement in fleet utilization, 25% gains in collection efficiency, and up to 48% reduction in overflow incidents. AI sorting studies also report controlled classification accuracies above 90%, while recent techno-economic evidence indicates 95.1% material purity, 50 items/minute throughput, and payback periods of 4.3-4.9 years under specific emerging-economy conditions. The review concludes that AIoT can improve municipal waste services only when technical performance guarantees are combined with open data standards, cybersecurity safeguards, human oversight, and context-sensitive inclusion of informal waste actors.
Gouskir et al. (Thu,) studied this question.