Intelligent Edge Computing for IOT: AI-Powered Decision Making at the Edge

The integration of Artificial Intelligence (AI) and Edge Computing in Internet of Things (IoT) systems has emerged as a transformative solution to address the limitations of cloud-centric architectures, such as high latency, bandwidth constraints, and security vulnerabilities. AI-powered edge computing enables real-time data processing and intelligent decision-making by executing machine learning and deep learning models directly on edge devices. This approach enhances efficiency, scalability, and privacy, making it ideal for smart cities, healthcare, industrial automation, autonomous vehicles, and smart homes. However, several challenges persist, including resource constraints, AI model optimization, security risks, interoperability issues, and explainability concerns. This paper explores the architecture, applications, challenges, and future research directions in AIdriven edge computing, highlighting emerging trends such as federated learning, blockchain security, and energy-efficient AI models. As AI at the edge continues to evolve, it will play a pivotal role in enhancing real-time intelligence, automation, and security in next-generation IoT ecosystems.