Smart Emergency introduces an AI-driven structure to adapt to emergency operations through real-time data analysis, IoT and telemedicine. System AI-driven vehicle routing, predictive analysis and dynamic traffic control increase the detection, resource allocation and medical decision-making. Unlike traditional approaches, Smart Emergency dynamically prioritizes emergency interventions, improving response times and patient outcomes. A functional prototype has been developed and tested using controlled test scenarios and applied to the street network of the city of Rabat. This prototype includes a hybrid optimization engine for vehicle dispatch, a dynamic mapping interface, and algorithms for identifying high-risk zones. Although not yet deployed in real-world emergency networks, the system demonstrates the feasibility of the proposed architecture and serves as a foundation for future pilot deployments. This study presents the system architecture, core components, and current implementation status, highlighting the platform’s potential scalability for both routine and large-scale crises. Smart Emergency introduces an AI-driven structure to adapt to emergency operations through real-time data analysis, IoT and telemedicine. System AI-driven vehicle routing, predictive analysis and dynamic traffic control increase the detection, resource allocation and medical decision-making. Unlike traditional approaches, Smart Emergency gives dynamic emergency interventions, improves response time and patient results.