Integrating Artificial Intelligence with Edge Computing for Scalable Autonomous Networks

The integration of Artificial Intelligence (AI) with Edge Computing is transforming autonomous networks by enabling real-time decision-making, reducing latency, and improving scalability. Traditional cloud-based AI processing often suffers from bandwidth constraints, high latency, and security vulnerabilities, limiting its effectiveness for dynamic and resource-constrained environments. By leveraging AI at the edge, data processing occurs closer to the source, enabling faster insights, enhanced network resilience, and optimized resource utilization. This paper explores the synergy between AI and Edge Computing, detailing how machine learning algorithms, federated learning, and intelligent orchestration frameworks enhance autonomous network performance. We discuss key architectural considerations, security implications, and real-world applications in smart cities, industrial IoT, and 5G/6G networks. The study highlights challenges such as interoperability, energy efficiency, and AI model optimization while proposing strategies to address these issues. Our findings underscore the transformative potential of AI-driven edge computing for next-generation autonomous networks, paving the way for more adaptive, efficient, and scalable systems.