The rapid growth of modern communication networks, driven by increasing data traffic, cloud computing, and IoT devices, has made traditional network management approaches insufficient to handle complexity and scalability challenges. Intelligent network management using machine learning (ML) offers a dynamic and automated solution for monitoring, analyzing, and optimizing network performance. This paper explores how ML techniques such as supervised learning, unsupervised learning, and reinforcement learning can be applied to tasks including traffic prediction, anomaly detection, fault diagnosis, and resource allocation. By leveraging real-time and historical network data, ML-based systems can identify patterns, predict potential failures, and adapt network configurations proactively. The study also examines challenges such as data quality, model interpretability, and integration with existing network infrastructures. Overall, intelligent network management systems enhance reliability, efficiency, and scalability, enabling next-generation networks to meet evolving demands.