Applications of artificial intelligence in urban solid waste management: A systematic literature review

Abstract

This research examines the application of artificial intelligence (AI) in urban solid waste management, focusing on innovative solutions that optimize processes and promote environmental sustainability. Through a systematic literature review, recent studies employing machine learning techniques, neural networks, and IoT sensors to transform traditional waste management systems are analyzed. The review covers research published between 2018 and 2025, selected from reputable databases such as Web of Science and Scopus, following PRISMA protocol guidelines to ensure the quality and relevance of the included works. The analysis highlights various areas of AI application, including waste collection route optimization, landfill site selection, supply chain improvement, and automated waste classification. These applications not only reduce operational costs and pollutant emissions but also foster the circular economy through more efficient recycling and material reuse. Additionally, real-time data integration facilitates strategic decision-making for infrastructure planning and resource allocation in complex urban environments. The study synthesizes current technological advances while identifying limitations such as dependence on data quality and availability and the need to expand access to diverse information sources. In conclusion, the incorporation of AI in solid waste management represents a transformative tool that can drive the development of smarter, more resilient, and sustainable cities.