International Journal of Scientific Research and Engineering Development

The increasing complexity of modern communication networks, driven by the rapid expansion of 5G technologies, the Internet of Things, and distributed cloud infrastructures, has exposed the limitations of traditional reactive network management approaches. Conventional management systems rely heavily on manual intervention and fixed rule-based policies, making them ill-equipped to address contemporary challenges such as excessive alarm generation, operational inefficiencies, and declining Quality of Service experienced by end users. This study explores the transformative potential of artificial intelligence and predictive analytics in addressing these challenges. It examines the architectural principles of AI for IT Operations frameworks, highlights the critical role of high-quality data in enabling reliable model performance, and reviews a range of machine learning techniques designed for predictive fault detection and proactive QoS management. Through the analysis of practical case studies, the paper demonstrates how AI-driven solutions enable a transition from reactive fault handling to proactive and self-healing network operations. The discussion also considers key barriers to large-scale adoption, including issues related to data reliability and the interpretability limitations often associated with complex models. The paper concludes by examining the emerging role of Large Language Models as a promising direction for enabling more transparent, explainable, and human-centered intelligent network management systems.

Dr.Deepak Tomar, Dr.Kismat Chhillar, Prof.Alok Verma, Dr.Anil Kewat,"Intelligent Network Fault Prediction and QoS Assurance Using Artificial Intelligence Techniques" International Journal of Scientific Research and Engineering Development, V9(2): Page(511-520) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.