The combination of Artificial Intelligence (AI) and Edge Computing is revolutionizing immediate data processing by enabling intelligent decision-making at the system edge. Unlike traditional cloud-based AI systems, edge computing decreases latency, enhances privacy, and optimizes bandwidth practice by processing data closer to the basis. This chapter explores the synergistic relationship between AI and Edge Computing, discussing key advancements in edge AI architectures, model optimization techniques, and deployment challenges. Furthermore, it highlights applications in healthcare, autonomous systems, smart cities, and manufacturing IoT, wherever real-time analytics and low-latency inference are serious. The chapter also examines the role of federated learning, and energy-efficient AI models in advancing edge AI capabilities. Future research directions, including AI-driven edge security, sustainable edge AI, and the convergence of 5G and AI at the edge, are also discussed.