Artificial intelligence (AI) is rapidly transforming the landscape of sustainable waste management, yet fragmented adoption, limited infrastructure, and contextual disparities continue to pose significant global challenges. This review investigates the integration of AI in waste collection, sorting, recycling, and treatment, aiming to identify the innovations, strategies, and future directions essential for enhancing sustainability. Addressing a critical gap in comparative literature, this study uniquely examines global AI applications across municipal, industrial, agricultural, and nuclear waste sectors, blending both qualitative and quantitative insights. Through a systematic synthesis of recent studies, the review highlights trends such as real-time monitoring, smart sensor deployment, predictive modeling, and AI-IoT-blockchain integration, revealing substantial gains in operational efficiency, fault detection, route optimization, and environmental compliance. Contradictions emerge regarding implementation success in developed versus developing nations, with affordability and digital readiness influencing outcomes. While cognitive systems and decision-support algorithms show promise in improving circular economic practices, unresolved concerns around data privacy, scalability, and inclusivity remain. The findings suggest that AI not only boosts resource recovery and emission reduction but also supports dynamic, data-driven policy frameworks. Despite limitations in long-term field data and regional disparities, the study emphasizes the importance of cross-sector collaboration, stakeholder engagement, and adaptive innovation. Future research should prioritize inclusive technologies, real-time decision-making, and context-sensitive simulation models. Ultimately, this review provides a strategic roadmap to harness AI for a more equitable, efficient, and sustainable global waste management future.
Jack Ng Kok Wah (Sat,) studied this question.