<![CDATA[Disasters such as earthquakes pose severe threats to sustainable urban development, causing loss of life, infrastructure collapse, and long-term disruption. In the critical first hours after an earthquake, delays in emergency response significantly reduce survival chances for vulnerable populations. This paper proposes an integrated framework that leverages internet of things (IoT)-enabled ambulances, geographic information systems (GIS), and optimization algorithms to enhance post-earthquake emergency response. The framework addresses two key challenges: i) optimal allocation of ambulances under resource constraints and ii) dynamic routing in disrupted road networks. Using real data from the 2023 Al Haouz earthquake in Morocco, the study compares deterministic approaches (Dijkstra, A*) with metaheuristics (particle swarm optimization (PSO), ant colony optimization (ACO), and Tabu Search (TS)). Results show that PSO reduced ambulance requirements by 40% while rescuing more citizens, and ACO achieved the highest route reliability (0.96). These findings demonstrate the practical applicability of IoT-enabled smart ambulances in improving resilience, efficiency, and equity in urban disaster management.]]>
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