The integration of electric vehicles (EVs) into modern transportation systems necessitates sophisticated energy management strategies to optimize their performance and sustainability. This chapter explores the pivotal role of Artificial Intelligence (AI) in advancing smart energy management for EVs. Covering various aspects of AI applications, such as predictive modeling for energy consumption, adaptive control strategies, genetic algorithms for charging station optimization, and integration of AI techniques for synergistic benefits. Beginning with predictive modeling, we explore how AI augments predictive analytics in EVs, delving into data sources, feature selection, and applications in forecasting energy consumption and scheduling maintenance tasks. This chapter further delves into AI-driven adaptive control strategies, detailing the techniques involved in managing EV energy efficiently, including dynamic energy distribution algorithms, optimal charging strategies, and real-time adaptation to grid conditions and user preferences. The chapter also explores genetic algorithms for optimizing the locations of charging stations, covering an overview of genetic algorithms, the factors influencing optimization decisions, and the objectives and constraints involved. Additionally, it investigates the integration and synergies among various AI techniques in EV energy optimization, discussing potential combinations, and synergies between battery management.