Efficient emergency medical services (EMS) are critical to saving lives. Delays in ambulance routes and inadequate patient monitoring during transit are two of the most pressing challenges in the EMS domain. This paper presents an integrated system that combines Artificial Intelligence (AI), specifically Ant Colony Optimization (ACO), real-time ambulance routing, IoT-based patient monitoring, and smart traffic management to enhance EMS performance. The system dynamically calculates optimal ambulance routes in real time while simultaneously monitoring patient vitals through IoT-enabled sensors. Predictive analytics, powered by machine learning, allows for early detection of patient deterioration, enabling better preparation and faster intervention by hospital staff. Layered software architecture ensures the platform’s scalability, security, and interoperability. Pilot testing demonstrated significant improvements in response time and patient outcome reliability. This work offers a scalable, intelligent EMS platform that aligns with smart city initiatives and digital healthcare transformation. An empirical validation through simulation showed substantial reductions (80%–88%) in ambulance response times in selected urban neighborhoods. This innovative solution not only enhances EMS efficiency but also contributes to reducing mortality rates in critical medical emergencies.