A BSTRACT 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. Keyword: Predictive maintenance, network monitoring, anomaly detection, artificial intelligence (AI), machine learning (ML), network fault prediction, proactive network management, data-driven maintenance, telecom network analytics. International Journal of Technology, Management and Humanities (2025) DOI: 10.21590/ijtmh.11.02.08 Predictive Network Maintenance and Anomaly Detection with AI Oluwatosin Oladayo Aramide Network and Storage Layer, Netapp Ireland Limited, Ireland. International Journal of Technology, Management and Humanities RESEARCH ARTICLE International Journal of Technology, Management and Humanities Volume 11, Issue 2, 2025 I NTRODUCTION Modern digital infrastructures are increasingly reliant on the seamless operation of interconnected network systems, particularly in sectors such as telecommunications, manufacturing, energy, and logistics. As these networks grow in complexity and scale, they become more susceptible to failures, performance degradation, and cybersecurity threats. Traditionally, maintenance in such systems has been reactive, responding only after faults occur which often leads to extended downtime, increased operational costs, and compromised service reliability (Garcia, Sanz-Bobi, & Del Pico, 2006; Rachmad, 2015). The emergence of artificial intelligence (AI) and machine learning (ML) presents a paradigm shift toward predictive and prescriptive network maintenance. These technologies enable systems to analyze massive streams of real-time and historical data, identify patterns indicative of potential failures, and take preemptive action to mitigate disruptions (Çınar et al., 2020; Afridi, Ahmad, & Hassan, 2022). Advanced ML algorithms including long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) have shown strong potential in detecting anomalies and forecasting equipment behavior in high- volume, data-driven environments (Choi et al., 2021; Geiger et al., 2020). This article investigates the role of AI/ML models in enhancing predictive maintenance and anomaly detection capabilities within network infrastructures. It focuses on their deployment across various architectures, such as edge-cloud hybrid systems, and evaluates their performance in real-time applications like smart grids, sensor networks, and industrial monitoring systems (Sathupadi et al., 2024; Omol, Mburu, & Onyango, 2024). By transitioning from reactive responses to prescriptive interventions, AI-driven network maintenance holds the promise of more resilient, adaptive, and cost- efficient digital ecosystems. Theoretical and Technical Foundations The integration of Artificial Intelligence (AI) and Machine Learning (ML) in predictive maintenance and anomaly detection systems is grounded in a convergence of