The increasing adoption of electric vehicles (EVs) presents challenges in optimizing charging infrastructure and route planning to enhance efficiency, reduce operational costs, and improve user experience. This research explores advanced optimization techniques, including Mixed-Integer Linear Programming (MILP), metaheuristics, and machine learning-based approaches, to develop adaptive and efficient EV routing and charging solutions. Through the integration of actual time variables including traffic patterns, cost of energy fluctuations, and the accessibility of charging stations, this suggested model improves choice-making in dynamic circumstances. Simulation results demonstrate significant improvements in travel time, energy efficiency, and overall system performance compared to traditional approaches. The findings contribute to the development of intelligent EV transportation networks that support sustainable mobility solutions.