Traffic congestion poses a significant challenge to efficient emergency response in urban areas. This project introduces an AI-driven system designed to optimize traffic signals from Changanacherry to Pushpagiri Medical College Hospital Thiruvalla. Leveraging YOLOv5, an advanced object detection model, the system identifies and tracks emergency vehicles in real time through traffic camera feeds. Integrated with Python in an Anaconda environment and developed using the Spyder IDE, the system dynamically manages traffic signals by extending green lights, adjusting signal durations, and clearing intersections to facilitate uninterrupted emergency routes. The project includes code hosted on GitHub for collaborative development and version control. By incorporating predictive analytics, the system enhances traffic management by analyzing congestion patterns and improving signal timings to ensure efficient emergency vehicle movement. This approach significantly reduces emergency response times while improving overall traffic flow. The project highlights a scalable and adaptable solution for smarter, safer urban transportation networks by combining object detection, analytics, and real-time decision-making.