International Research Journal of Education and Technology

The growing adoption of the Internet of Things (IoT) has resulted in an explosion of data, necessitating efficient, real-time decision-making capabilities. Traditional cloud-based processing models introduce latency, bandwidth limitations, and security vulnerabilities. Edge AI, which integrates artificial intelligence (AI) with edge computing, enables data processing directly on IoT devices or nearby edge nodes, significantly reducing dependence on cloud infrastructure. This paper explores the architecture, benefits, and challenges of Edge AI in real-time IoT applications. Key advantages include low latency, reduced bandwidth consumption, enhanced security, and improved energy efficiency. We also discuss real-world applications such as smart healthcare, autonomous vehicles, industrial automation, and smart cities. Despite its advantages, Edge AI faces challenges, including computational constraints, model optimization, and security risks. Future research should focus on lightweight AI models, 5G integration, federated learning, and specialized AI hardware to enhance Edge AI’s efficiency and scalability.