Abstract : The integration of artificial intelligence (AI) in waste management is revolutionizing the sector, enhancing sustainability and operational efficiency across various domains, such as municipal, industrial, and wastewater treatment. Despite AI's potential, challenges such as outdated infrastructure, manual labor reliance, and the lack of integration with complementary technologies like IoT and blockchain impede its widespread adoption. The review aims to explore the role of AI in optimizing waste collection, sorting, and resource recovery, focusing on technologies like machine learning, predictive analytics, and real-time monitoring. The novelty of the review lies in highlighting the integration of AI with IoT and blockchain, emphasizing how such synergies can unlock AI's transformative potential in waste management. The methods section details a systematic review on AI and machine learning applications in waste management, focusing on economic, environmental, and operational aspects. It defines eligibility criteria to ensure relevance, quality, and methodological rigor, emphasizing recent peer-reviewed studies. The review process involves database searches, full-text assessments, and a quality check. Data extraction captures key findings, methodologies, and AI technologies, while synthesis integrates qualitative and quantitative analyses. Results highlight AI's impact across sectors, offering insights into enhancing efficiency, sustainability, and the circular economy in waste management. The findings show that AI-driven waste management systems, including predictive modeling, AI algorithms, and smart sensors, significantly enhance waste sorting, resource allocation, and operational efficiency. Notably, AI-powered waste management systems have improved environmental and economic outcomes globally. In the USA, AI and IoT reduced waste collection costs by 30% and increased recycling by 25%. In Africa, AI enhanced resource allocation, boosting operational efficiency by 40%. In Asia, AI in wastewater treatment reduced hazardous discharge by 35%. These innovations support circular economy goals, reducing carbon footprints and improving sustainability. The review concludes that AI's contribution to the circular economy is evident, with long-term benefits such as reduced environmental impact, improved efficiency, and cost savings. However, the initial investment remains a limitation, particularly for resource-constrained municipalities. Future research should focus on advancing waste-to-energy technologies, enhancing recycling systems, and improving blockchain integration for waste traceability. AI's role in zero-waste cities and smart resource recovery systems also holds promise for driving sustainable waste management practices. In sum, while the integration of AI in waste management offers substantial benefits, overcoming financial, technological, and social barriers is crucial for its successful and widespread implementation.