AI-Assisted Waste Management Systems: A Comprehensive Review of Technologies, Case Studies, and Sustainability Impacts

The effects of urbanization, population increase, and industrialization have also contributed to massive production of complex wastes that are overstressing the conventional methods used in the management of such wastes. The conventional methods for waste management, which involve manual processes dependent on time, may not cope well with issues regarding inadequacies in resource management, overflowed bins, contaminated water as well as environment, and health aspects. This report highlights the latest innovations in using AI for the support of waste management in the context of real-time generation and cleanup of wastes, use of predictive analytics, design of sensor networks and implementation of the internet of things, and intelligent automation. The implementation of sensors in AI helps in real-time generation and cleanup of wastes through ultrasonic sensors, load cell sensors, and GPS tracking devices. These are in turn used again for the implementation of AI algorithms for cleanup route optimizations in the vehicles, wastages in cleanup transports, preventing overflows, and operation maximization. The predictive analytics module, developed through machine learning algorithms, helps in precise forecasting of future generation of wastes based on historical inputs and season changes. The literature also offers evaluation analysis concerning the application of Deep Learning as well as Computer Vision