Artificial intelligence (AI)-optimized optimization has transformed EV charging by minimizing inefficiency, reducing costs, and using renewable energy. Traditional charging stations follow fixed schedules independent of dynamic EV demand and grid conditions. Predictive analytics, reinforcement learning, and scheduling algorithms powered by AI enable real-time decision-making that optimizes the charging slots as a function of electricity prices, renewable supply, and user requests. By charging during periods of high renewable energy generation, AI minimizes carbon footprint while injecting stability into the grid. Vehicle-to-grid (V2G) technology also makes EVs possible as rolling energy storage devices, encouraging better distribution of energy. Mobile apps with AI further increase customer satisfaction through suggestions of the best time to charge, reducing wait, and station congestion. Empirical research indicates that AI-based scheduling boosts station efficiency by 30% and reduces operating costs by 20%. Nevertheless, issues such as computational complexity, privacy of data, and integration into infrastructure continue. These problems can be alleviated through federated learning and decentralized AI designs. The future holds blockchain-based charging transactions and mixed AI models that combine deep learning and real-time optimization. In general, AI-based EV charging is critical for sustainable, efficient, and cost-effective energy management with smooth integration into smart grids and renewable energy.