Smart Waste Management Systems Using Big Data And Machine Learning Technologies

Abstract

Rapid urbanization and population growth have intensified the challenges of waste management across the globe. Traditional waste management systems often lack the intelligence and efficiency to handle modern waste generation patterns. In response, the integration of big data analytics and machine learning (ML) technologies into waste management has emerged as a transformative approach. This paper explores how smart waste management systems utilize real-time data collection, predictive analytics, and intelligent decision-making to enhance waste collection, reduce operational costs, and support environmental sustainability. The study highlights various applications of ML, including waste sorting, route optimization, and predictive maintenance, supported by big data platforms. It further discusses the challenges, such as data privacy, interoperability, and infrastructure limitations, and offers future directions for research and implementation. By leveraging digital intelligence, smart waste management represents a vital step toward achieving cleaner, smarter, and more sustainable cities.