As urban traffic management becomes increasingly complex, the effective response of emergency service vehicles through complex road networks becomes a critical challenge. This project introduces a novel approach using reinforcement learning techniques to optimize lane change decisions for emergency service vehicles in the SUMO simulation environment. The reinforcement learning model is trained to make intelligent lane change decisions based on a reward structure designed to prioritize efficient response times while minimizing disruptions to overall traffic flow.