The rapid evolution of communication networks, particularly with the introduction of 5G and the anticipated arrival of 6G, has introduced new complexities in managing traffic, routing, and resource allocation. Distributed Artificial Intelligence (DAI) is emerging as a promising solution to address challenges related to scalability, adaptability, and performance in these dynamic environments. With the unpredictable nature of network traffic patterns and the dynamic infrastructure of modern networks, effective network management is crucial for ensuring optimal resource utilization and preventing congestion. This is essential to maintain high performance, reliability, and scalability in today’s communication systems. This paper explores the application of AI techniques in network management, with a focus on key areas such as congestion control, routing management, and traffic prediction. By examining both centralized and distributed AI approaches—such as Multi-Agent Reinforcement Learning (MARL) —it highlights their potential to enhance network efficiency, improve latency, increase throughput, and reduce packet loss. The paper also addresses the limitations of current methods, while discussing potential future directions for AI- driven solutions in large-scale, real-time network operations.