Urban intersections face persistent challenges due to traffic congestion, which affects travel efficiency and increases delays. This study presents a Deep Reinforcement Learning (DRL)-based approach to optimize traffic signal timing within a SUMO (Simulation of Urban MObility) simulation environment. The suggested method focuses on minimizing vehicle delays, waiting times, and congestion through real-time adaptive control of signal phases. The system is modeled around a four-way intersection with distinct signals for straight and left-turn lanes. A Deep Q-Network (DQN) is trained using a reward mechanism that incentivizes smoother traffic flow and reduced wait times. The model leverages state representations derived from vehicle occupancy and distance from traffic lights, and incorporates experience replay for consistent learning. One key aspect of the system suggested is the prioritization of emergency vehicles like ambulances by dynamically modifying signal phases to provide immediate passage. Experimental results show drastic improvements over conventional methods with reduced queue length, higher throughput, and quicker emergency response. This research offers a scalable and resource-efficient solution for modern urban traffic management, with potential applicability to realworld scenarios involving dynamic and mixed traffic conditions.