The aim of this paper is to present a novel solution to the problem of free traveling paths for emergency vehicles like ambulances. In India, due to heavy traffic congestion in cities and towns, ambulances get stuck in traffic jams, which can lead to failure in reaching the medical care destination in time. When there are delays in providing emergency medical services to people who require urgent medical attention, it can result in a failure to save their lives. To address this issue, we propose a system that utilizes real-time ambulance location data such that the corresponding traffic signals at intersections in the emergency vehicle traveling path are adjusted dynamically, which will provide priority to emergency vehicles and ensure that they can navigate congested roads efficiently. The timely delivery of emergency medical care is critical to saving lives and reducing long-term morbidity. This proposed approach leverages advanced data analytics, machine learning techniques, and vehicle-to-infrastructure (V2I) models to analyze traffic patterns and adjust signal timings on-the-fly, allowing emergency vehicles to move quickly and safely through urban areas. By implementing this solution, we aim to reduce response times and improve patient outcomes in emergency medical situations.