The rapid advancement of artificial intelligence (AI) and edge computing is shaping the future of Industry 5.0, where real-time, decentralized processing is crucial for intelligent decision-making. Edge AI, which combines AI capabilities with edge computing, reduces latency, enhances security, and enables energy-efficient data processing directly at the source. This shift allows industries to overcome traditional cloud-based limitations, supporting applications in industrial automation, healthcare, smart cities, and cybersecurity. However, the widespread adoption of Edge AI introduces challenges such as computational constraints, energy efficiency, data security, and scalability. To address these issues, researchers are exploring solutions such as neuromorphic computing, federated learning, and 6G-enabled ultra-low-latency communication. Additionally, explainable AI (XAI) is emerging as a key factor in ensuring transparency and trust in AI-driven decisions. This paper provides a comprehensive analysis of Edge AI’s role in Industry 5.0, examining its technological advancements, practical applications, and potential challenges. It also highlights future research directions that will shape the evolution of Edge AI, ensuring its scalability, security, and efficiency. By bridging the gap between AI and real-time edge processing, Edge AI is set to become a cornerstone of next-generation intelligent systems, transforming industries and enabling human-centric automation.