The transition to sustainable transportation is increasingly reliant on the integration of electric vehicles (EVs) into existing energy systems. Efficient management of EV charging infrastructure is imperative to optimize energy usage, minimize grid stress, and enhance user convenience. In this chapter, we delve into the application of artificial intelligence (AI) in driving optimization techniques for EV charging infrastructure. AI, with its adaptive and predictive capabilities, offers transformative solutions to address the complex challenges associated with EV charging. Through the utilization of AI-driven algorithms such as reinforcement learning, genetic algorithms, and neural networks, charging networks can be dynamically optimized to maximize efficiency and reliability. This chapter explores various AI-driven optimization techniques tailored specifically for EV charging infrastructure. Reinforcement learning algorithms enable charging stations to learn and adapt their behavior based on real-time feedback, resulting in improved scheduling and resource allocation. Genetic algorithms provide an evolutionary approach to optimizing charging infrastructure layout and operation, considering factors such as charging demand, grid capacity, and geographical constraints. Neural networks offer predictive capabilities for forecasting charging demand, optimizing charging schedules, and mitigating peak load demands on the grid. Real-world implementations and case studies are examined to showcase the effectiveness of AI-driven optimization techniques in EV charging infrastructure. Moreover, challenges such as data privacy, interoperability, and scalability are addressed, along with potential solutions and future research directions. The chapter underscores the pivotal role of AI in shaping the future of EV charging infrastructure, facilitating the seamless integration of electric vehicles into the broader energy ecosystem.