Recent research in Deep Reinforcement Learning (DRL) for Traffic Engineering focuses on optimizing vehicular mobility, congestion control, and adaptive traffic signal management through intelligent decision-making frameworks. DRL models such as Deep Q-Networks (DQN), Actor-Critic, and Proximal Policy Optimization (PPO) are being applied to learn dynamic traffic patterns and enhance real-time route optimization, congestion avoidance, and vehicle coordination in complex road networks. Studies integrate DRL with edge computing, federated learning, and Internet of Vehicles (IoV) frameworks to enable decentralized traffic control and improve scalability, privacy, and energy efficiency. These approaches achieve superior performance in traffic flow prediction, adaptive signal timing, and multi-agent cooperation compared to traditional control algorithms, positioning DRL as a key enabler of next-generation intelligent transportation systems.