Urban traffic congestion critically delays emergency service response times, leading to severe consequences. To address this, we propose a real-time intelligent system that enhances emergency vehicle transit through automated detection and dynamic route guidance. The system utilizes a YOLOv8 deep learning model to accurately identify ambulances, police cars, and fire trucks from live CCTV footage, demonstrating robust performance under challenging conditions such as varying lighting, visual noise, and dense traffic. Upon detection, the system dynamically computes the shortest viable route to the nearest relevant facility (e.g., a hospital) using the OpenRouteService (ORS) and Overpass API. To ensure rapid public cooperation, the suggested route is communicated through two primary channels: it is displayed on traffic signal LED screens and encoded into a QR code for direct mobile navigation. Our model achieves a high detection accuracy of mAP@0.5 = 0.984 and mAP@0.5:0.95 = 0.82. By integrating a state-of-the-art detection model with user-friendly routing interfaces, this framework significantly reduces emergency response delays, improves transit efficiency, and has the potential to save lives. Future work will focus on integrating live GPS data from emergency vehicles and expanding the system’s operational scope.