The widespread adoption of electric vehicles (EVs) is revolutionizing the transportation industry while putting tremendous pressure on current energy infrastructures. With EV charging behaviors becoming more dynamic and sophisticated, smart and scalable solutions are needed to maintain grid stability and energy efficiency. This paper provides an in-depth overview of state-of-the-art EV charging optimization methods with a focus on artificial intelligence (AI) and machine learning (ML) methods. We suggest a classification model that separates optimization techniques into scheduling-based and energy management-based categories, covering a broad spectrum of algorithms ranging from heuristic models to deep reinforcement learning and real-time predictive analytics. These AI-based methods improve load forecasting, minimize energy expenses using dynamic pricing, stabilize demand and supply, and enable integration with renewable energy sources like solar and wind. Emerging technologies such as vehicle-to-grid (V2G), IoT-enabled infrastructure, and edge computing also facilitate real-time, adaptive decision-making for smart charging systems. Our results indicate the increasing promise of AI-based optimization in creating a sustainable, efficient, and resilient EV ecosystem that aligns with global energy transition objectives.The application of digital twins and simulation platforms enables better testing of EV-grid interactions before mass deployment. AI also enables user behavior and location-based personalized charging strategies, which enhance user satisfaction.