The rise of autonomous mobility has increased the demand for efficient energy management in Connected and Automated Electric Vehicles (CAEVs) and Unmanned Aerial Vehicles (UAVs). Traditional plug-in charging is often impractical due to fixed locations and operational downtime, driving interest in wireless solutions such as laser beaming and magnetic resonance charging. This article explores Deep Reinforcement Learning (DRL)-based wireless charging strategies within Intelligent Transportation Systems (ITS), leveraging Internet of Things (IoT) technologies for realtime energy optimization. DRL enables adaptive charging for ground-based, UAV-to-UAV, and UAV-to-CAEV energy transfer, enhancing efficiency and sustainability. However, challenges such as scalability, fairness, and real-time decision-making remain. Future research directions, including hybrid DRL-optimization models and federated learning, are discussed to improve system reliability and performance. As autonomous mobility evolves, DRL based wireless charging will be essential for seamless, resilient, and sustainable energy solutions in next generation ITS.