Currently, Electric Vehicles (EVs) face limitations in terms of lower acceptance due to conventional charging stations and degradation, and low efficiency of batteries. The contributions of this research are in establishing an enhanced AI system, which comprises the present Autonomous Battery Swap Stations with the Solar Energy Integration Module. By employing AI techniques that include neural networks, reinforcement learning, and decision trees, the system predicts battery state of health, charge/discharge cycles, and solar power consumption in order to minimize electrical grid dependency. Simulation and case studies for the evaluation of the proposed system showed an improvement in energy efficiency up to 15%, while battery degradation by 25–30% and an increase in battery life by 20%. Therefore, this paper would contribute positively toward the sustainable development of the necessary infrastructures for the support of electrical transportation systems through providing a scalable, efficient green solution for the management of the EV batteries.