The escalating complexity of modern network infrastructures, characterized by the convergence of 5G, software-defined networking (SDN), and hyperscale cloud-to-edge continuums, has rendered traditional reactive maintenance models obsolete. In these high-velocity environments, a single link failure or hardware malfunction can trigger a cascade of service disruptions, resulting in significant financial losses and reputational damage. This review examines the paradigm shift toward Predictive Network Failure Analysis (PNFA) powered by Machine Learning (ML). By leveraging high-fidelity telemetry data, including syslog entries, SNMP traps, and flow metrics, ML models can identify the subtle \\\"pre-cursor\\\" signatures of impending hardware exhaustion, optical signal degradation, or software anomalies. This article categorizes current methodologies, focusing on the use of Long Short-Term Memory (LSTM) networks for temporal fault forecasting and Random Forests for multi-variate root cause analysis. We explore how predictive models enable the transition from \\\"Break-Fix\\\" to \\\"Proactive Remediation,\\\" where maintenance is triggered by a probability score rather than a catastrophic event. Furthermore, the review addresses critical challenges, such as the \\\"data imbalance\\\" problem, where failure events are rare compared to normal operations, and the necessity for Explainable AI (XAI) to ensure operator trust in automated diagnostics. By synthesizing recent academic breakthroughs and industrial frameworks, this paper provides a strategic roadmap for building \\\"Self-Healing Networks.\\\" The findings suggest that ML-driven predictive analysis significantly reduces the Mean Time to Repair (MTTR) and improves overall network availability, providing the cognitive foundation required for the next generation of autonomous digital infrastructure.