Artificial Intelligence for Predictive Failures of Network Devices: A Machine Learning Approach to Proactive Maintenance

Ensuring the reliability of network devices, such as routers, switches, and firewalls, is a critical challenge in modern IT infrastructure. Traditional network monitoring approaches rely on reactive failure detection, which often results in service disruptions, financial losses, and increased maintenance costs. This paper presents an AI-driven predictive maintenance framework that leverages machine learning (ML) models, including Random Forest, Gradient Boosting, and Recurrent Neural Networks (RNNs), to forecast failures before they occur. By analysing real-time performance metrics such as CPU utilization, memory consumption, system logs, and network traffic, the proposed system detects anomalies indicative of potential failures, enabling proactive interventions. The study evaluates the effectiveness of various ML models on real-world datasets, achieving a failure prediction accuracy of up to 95%. This research also addresses ethical considerations, including data privacy, algorithmic bias, and transparency, to ensure responsible AI deployment in network operations. The proposed solution contributes to enhancing network reliability, reducing downtime, and optimizing operational efficiency. This work demonstrates that AI-powered predictive maintenance offers a cost-effective, scalable, and intelligent approach to network failure prevention.