The rapid adoption of electric vehicles (EVs) has intensified the demand for intelligent routing systems capable of minimizing travel time, reducing energy consumption, and ensuring reliable access to charging infrastructure. Conventional routing algorithms often fail to incorporate dynamic, real-world constraints such as traffic fluctuations, charger availability, environmental conditions, and the nonlinear nature of EV energy usage. To address these limitations, this paper proposes a Quantum-Enhanced Deep Reinforcement Learning Transformer framework integrated with explainable AI for efficient EV routing across the Delhi-NCR megaregion. The model combines quantum-inspired optimization, deep neural policy networks, and real geospatial charging-station data, enriched with live weather intelligence to accurately estimate energy demand. Experimental evaluation shows that the proposed Q-DRL policy achieves an average energy savings of 1.5171 kWh per trip, equivalent to a 12.04% improvement compared to a baseline approach, highlighting the model's ability to generate energy-optimal, trustworthy routes. This study provides a scalable, data-driven foundation for next-generation EV mobility, supporting smarter and cleaner urban transportation.