Abstract

Emergency response times play a critical role in saving lives, especially in urban settings where traffic congestion and unpredictable events can delay ambulance arrivals. This paper explores a novel framework for smart route optimization for emergency vehicles, leveraging artificial intelligence (AI), Internet of Things (IoT) technologies, and dynamic traffic analytics. We propose a real-time adaptive routing system that integrates machine learning (ML) for predictive modeling and IoT-enabled communication with traffic infrastructure. The system is evaluated using simulated urban environments, achieving a 35% reduction in response times compared to traditional methods. This work lays the foundation for future advancements in intelligent emergency systems and their integration into smart cities.