A Novel Ai-driven Framework for Efficient Urban Waste Management Promoting Sustainability

Abstract

The pace of urbanisation and population growth has significantly increased the complexity of municipal solid waste management, leading to inefficiencies in the segregation, collection, and disposal of waste. Traditional waste management systems are mostly manual, time-consuming, error-prone, and environmentally unsustainable. To overcome these issues, this paper proposes a new AI-driven smart waste management framework to improve operational efficiency and support sustainable urban areas. The suggested framework combines artificial intelligence methods with intelligent sensing infrastructure to enable autonomous waste classification, real-time bin tracking, predictive waste-level analysis, and efficient collection planning. Compared with current waste management strategies that consider different elements of the problem separately, the proposed system offers an intelligent architecture that integrates decision-making, prediction, and sustainability into a single framework. The proposed framework is evaluated analytically to assess its anticipated performance in terms of efficiency, scalability, cost reduction, and environmental impact. Compared with conventional waste management methods, the analysis reveals potential advantages of smart practices that should be implemented in cities with AI assistance. The suggested framework can serve as a baseline for future implementation and experimentation and provide useful information to smart city designers, city leaders, and scholars seeking to achieve sustainable and intelligent waste management systems.