AI-Driven Garbage Control: Smart Collection, Sorting and Recycling for Sustainable Waste Management

Abstract

This study examines how Artificial Intelligence (AI) is reshaping waste management, a function that is central to keeping cities clean and sustainable. Conventional approaches depend heavily on manual effort and fixed schedules, which makes them slow and costly. Using a comparative analysis of 12 published sources, the study reviews AI applications in waste collection, sorting, recycling, and environmental monitoring. A smart bin fitted with fill-level sensors tells collectors where pickups are actually needed, so vehicles skip half-empty containers and overflow becomes less frequent. Sorting robots such as the ZRR2 deployed at the Ecoparc4 treatment plant in Barcelona rely on machine vision to separate mixed waste, which improves both recovery and the purity of the output. Citizen-facing tools such as the BinBin Helper applet in Hangzhou, China, extend the benefit to households through image recognition and text search, and the data they gather helps city planners understand waste generation. The comparison shows that AI can shorten collection routes, lower operating costs, and reach high sorting accuracy, although high set-up costs, uneven data quality, and limited policy support still slow adoption. The study concludes that AI can support more efficient, sustainable, and hygienic waste systems.