Smart and Sustainable E-Waste Management in Urban Areas: Integrating Data Science, IoT, and Machine Learning

Abstract

Electronic waste (E-waste) has become an important environmental and public-health concern because of rapid technological development, shorter product lifecycles, increasing consumption, and inadequate end-of-life management. Urban municipal corporations face particular difficulty because E-waste requires specialized collection, storage, transportation, sorting, recycling, and disposal practices. This paper presents a data-driven framework for improving E-waste management in urban India, with particular emphasis on the Mumbai Municipal Corporation (BMC). The proposed framework combines predictive analytics for forecasting E-waste generation, machine learning and image recognition for automated material sorting, route optimization for collection logistics, and Internet of Things (IoT) sensors for monitoring collection points. The framework is complemented by digital inventory management, resource recovery, sustainable product design, Extended Producer Responsibility (EPR), public awareness, and public-private collaboration. The objective is to move municipal E-waste management from a largely reactive process toward a proactive, technology-supported, and sustainable system.