Predictive Network Maintenance and Anomaly Detection with AI

Abstract

In an era defined by complex digital infrastructures, ensuring uninterrupted network performance has become a critical imperative. Traditional reactive maintenance models are increasingly inadequate in addressing the scale, speed, and sophistication of modern network failures. This article explores the transformative role of artificial intelligence (AI) and machine learning (ML) in predictive network maintenance and anomaly detection. It examines how intelligent algorithms analyze vast streams of network data to forecast potential failures, identify abnormal behavior, and enable proactive responses, ultimately shifting maintenance strategies from reactive to prescriptive. The study discusses advanced models including deep neural networks, autoencoders, generative adversarial networks (GANs), and transformer-based architectures that have demonstrated significant promise in forecasting system anomalies and optimizing infrastructure performance. Emphasis is placed on real-time applications across smart grids, sensor networks, industrial automation, and supply chain systems, with particular focus on the synergy between edge computing and cloud platforms in delivering scalable, low-latency solutions. Additionally, the article identifies key challenges, including data quality, model interpretability, and resource constraints, and proposes strategic frameworks for deploying AI-enhanced, self-healing networks. By integrating technological innovation with predictive analytics, organizations can significantly improve network resilience, reduce operational downtime, and create adaptive systems that learn and evolve. This study provides both theoretical insight and practical guidance for researchers, engineers, and decision-makers committed to building intelligent, autonomous network infrastructures for the future.