<p>Electric vehicles’ (EVs’) accelerated expansion is changing transportation systems and making managing the charging infrastructure and power grid much more difficult. In order to handle issues like demand variations, charging congestion, grid instability, intermittent renewable energy, and cybersecurity risks, intelligent coordination techniques are needed. Through data-driven forecasting, predictive control, and automated decision-making, artificial intelligence (AI) has become a crucial enabling technology for optimizing EV charging networks. An organized and thorough analysis of AI methods used in intelligent infrastructure for EV charging is presented in this research. System architecture, operational difficulties, optimization techniques, deployment obstacles, and new research avenues are all methodically examined in this paper. To guarantee openness and repeatability, a PRISMA-based approach for choosing literature is used. The scalability and practical viability of major AI technologies are compared, including deep learning for predictive maintenance, optimization-based scheduling, machine learning for demand forecasting, and reinforcement learning in order for real-time charging control. Additionally, this assessment highlights important implementation gaps in the areas of economic modeling, cybersecurity integration, distributed intelligence, and standards. Future research avenues for secure, self-optimizing, and autonomous charging ecosystems are explored. Academics, system operators, and politicians working toward the widespread implementation of AI-enabled EV infrastructure can use the findings as technical assistance.</p>