Rapid urban expansion and increasing population density have intensified the challenges associated with municipal solid waste management. Conventional waste collection practices, which depend on fixed schedules and manual supervision, often lead to inefficient resource allocation, delayed collection, overflowing bins, and environmental degradation. To address these limitations, this study presents a Smart Waste Management System that integrates Artificial Intelligence (AI) and the Internet of Things (IoT).

The proposed framework employs IoT-enabled smart bins fitted with sensors to continuously monitor parameters such as waste level, weight, and surrounding environmental conditions. The collected data is transmitted to a cloud-based platform for processing and analysis. Machine learning algorithms analyse real-time data to predict bin fill status, optimize collection routes, and estimate future waste generation trends. Additionally, image-based classification methods can be utilized to facilitate automatic segregation of biodegradable and non-biodegradable waste.

The integration of AI-driven analytics with real-time IoT monitoring enhances operational efficiency, reduces fuel consumption, minimizes collection time, and prevents bin overflow. The system contributes to improved urban sanitation, cost reduction, and environmentally sustainable waste management. Overall, the proposed approach supports smart city initiatives and aligns with sustainable development objectives by promoting data-driven and eco-friendly waste management practices.