Artificial Intelligence for Next-Generation Network Management: A Comprehensive Review of AI-Driven Automation, Optimization, and Self-Healing Networks
Abstract
The rapid growth of cloud computing, edge computing, Internet of Things (IoT), and 5G/6G technologies has significantly increased the complexity of modern communication networks. Traditional network management techniques, which rely heavily on predefined rules and manual configuration, are increasingly inadequate for managing dynamic, heterogeneous, and large-scale network environments. Artificial Intelligence (AI) has emerged as a promising approach for enabling intelligent network management through automated decision-making, predictive analytics, anomaly detection, resource optimization, and self-healing mechanisms. This review provides a comprehensive analysis of AI-driven network management techniques, covering machine learning, deep learning, reinforcement learning, and emerging generative AI approaches. The paper examines their applications in traffic engineering, fault management, configuration optimization, quality-of-service assurance, and network orchestration. It further compares existing approaches according to accuracy, scalability, computational requirements, adaptability, and real-time performance. Key challenges, including data availability, explainability, security, model generalization, and deployment overhead, are discussed. Finally, emerging research directions toward autonomous and self-optimizing networks are identified
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