AI-IoT-graph synergy for smart waste management: a scalable framework for predictive, resilient, and sustainable urban systems

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.