Leveraging AI for Sustainable Waste Management: Enhancing Recycling and Reducing Landfill Waste in Circular Economy Models

This paper reviews AI's revolutionary role in moving waste management towards a circular economy, with a particular emphasis on raising recycling rates and minimizing landfill trash. The study investigates a variety of AI methods, including Support Vector Machines (SVM), Random Forests (RF), Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Genetic Algorithms (GA), Decision Trees (DT), and K-Nearest Neighbors (KNN). These algorithms are useful for optimizing waste categorization, resource efficiency, and material recovery, especially in electronic waste recycling. The paper also explores how artificial intelligence (AI) may be integrated with Municipal Solid garbage Management (MSWM (Municipal Solid Waste Management)) systems to improve garbage monitoring, recycling, and reuse. Despite issues in data dependability and performance assessment, AI has considerable potential for developing more sustainable and effective waste management systems. The article focuses on AI's contributions to attaining circular economy goals such as resource efficiency, waste reduction, and sustainability, while also suggesting significant topics for future research.