In urban regions, traffic congestion is a serious issue. Every year, there is a 25-40% percent increase in the number of vehicles, aggravating problems like traffic jams, noise and air pollution, and travel delays. The traditional approach requires organizations such as traffic police to manually maintain and set the timings for red and green lights. Modern city traffic signals are inefficient and unable to deal with the aforementioned issues because they do not have automated processes. Because of this, traffic congestion is still a problem in many cities throughout the world, with poor traffic management and broken signals being the main causes. The main objective is that cities are looking at creative ways to address this issue, such as better infrastructure, adaptive signal timing, and intelligent traffic management systems. In the existing system, traffic is often navigated using ordinary GPS-based navigation devices in emergency response circumstances. These systems provide real-time route recommendations based on traffic data, but they might not be the greatest options in emergency situations where quick response is crucial. When there is heavy traffic, they usually struggle to adjust, which delays emergency vehicles. The proposedsystem aims to propose an Internet of Things-based intelligent traffic navigation to predict and manage accidents within congested traffic which shortens wait times in emergency situations by controlling traffic signals. Using real-time data and machine learning techniques, IoT-enabled intelligent traffic guidance is a considerable improvement over existing methods and can greatly improve emergency response capabilities during periods of excessive congestion. It has the potential to save lives by accelerating response times and ensuring that emergency vehicles reach at their destinations safely and on time.