AI-Powered Predictive Models for Network Fault Detection and Proactive QoS Management: A Comprehensive Analysis

Modern communication networks face increasing challenges in ensuring reliability and maintaining Quality of Service (QoS) as they scale across heterogeneous devices, services, and infrastructures. Traditional threshold-based monitoring and rule-driven systems, though valuable, lack the adaptability to foresee faults and prevent degradations before they occur. To address this gap, artificial intelligence (AI)-powered predictive models have emerged as promising solutions. By leveraging machine learning, deep learning, and reinforcement learning, networks can predict potential failures, detect anomalies with higher accuracy, and initiate corrective measures before service-level agreements (SLAs) are violated. These proactive strategies not only minimize downtime and resource wastage but also ensure seamless delivery of applications across critical domains, including 5G/6G communications, cloud-native infrastructures, and enterprise networks. This paper presents a comprehensive analysis of AI-driven predictive approaches for fault detection and proactive QoS management, examining their architectures, algorithms, and performance across diverse network environments. We investigate supervised and unsupervised learning models for fault classification, temporal prediction methods using recurrent neural networks, and reinforcement learning techniques for dynamic QoS optimization. The study evaluates these predictive frameworks on metrics such as detection accuracy, latency reduction, scalability, and resource efficiency. Beyond comparative analysis, the paper also proposes a hybrid AI-based framework that integrates fault prediction with QoS-aware decision-making to build self-healing and resilient network systems. The findings highlight that AI-enabled predictive management is a cornerstone for future intelligent networks, capable of delivering superior performance, adaptability, and operational resilience.