Effective waste management is essential for smart cities, but fixed collection schedules frequently result in missed pickups, overflow events, and inefficient fuel consumption. This study introduces a framework that integrates Artificial Intelligence (AI), Internet of Things (IoT) sensors, and graph-theoretic optimization. A simulated dataset of 500 bins across five zones was used to train an XGBoost classifier for overflow prediction, combined with spatial risk mapping and routing optimization on a weighted bin network. The AI model achieved high predictive accuracy (94.1%) and recall (95.8%), ensuring reliable identification of overflow-prone bins. Compared to a static collection model, the smart system reduced overflow events by 50%, missed pickups by 72.7%, and fuel usage by 15.5%, while improving bin utilization efficiency by 35.5%. These findings demonstrate that integrating AI, IoT, and graph-theoretic methods can significantly enhance operational efficiency and environmental sustainability in urban waste logistics. The framework provides a scalable solution that adheres to Industry 4.0 principles and serves as a foundation for future smart city infrastructures. The system’s modular architecture allows seamless integration with existing municipal platforms, enabling in real-time responsiveness and adaptive service delivery. By bridging operational decision-making with simulation-driven insights, the framework sets a precedent for data-driven governance in urban infrastructure.
Anitha et al. (2025) studied this question.