This article details an artificial intelligence-powered preventive maintenance system designed specifically for networking devices. As network infrastructure grows increasingly complex, traditional reactive maintenance approaches have proven inadequate for ensuring optimal performance and reliability. The system leverages advanced telemetry collection frameworks, machine learning algorithms, and predictive analytics to detect potential failures before they impact service quality. Through continuous monitoring of core system metrics, interface traffic data, and network-specific parameters, the system can identify anomalous patterns, forecast component degradation, and recommend appropriate remediation actions. The implementation methodology encompasses comprehensive data collection, baseline establishment, model development, and training phases. Alert classification mechanisms prioritize issues based on severity while automated response capabilities translate analytical insights into actionable maintenance strategies. Performance metrics demonstrate significant improvements in network availability, maintenance efficiency, and operational costs compared to traditional approaches, highlighting how AI-driven preventive maintenance is transforming network operations.