The evolution of modern digital ecosystems necessitates a transition from reactive maintenance to proactive, intelligent oversight. Intelligent Infrastructure Monitoring (IIM) integrates Artificial Intelligence (AI) and high-fidelity telemetry to provide real-time visibility into complex, distributed environments. This review examines the convergence of automated data collection and machine learning algorithms in optimizing system reliability. By leveraging granular telemetry data—including metrics, logs, and traces—AI models can identify anomalies, predict hardware failures, and automate remediation processes. The integration of these technologies reduces operational overhead and minimizes downtime in critical sectors such as telecommunications, cloud computing, and smart cities. This article explores the architectural frameworks of AI-driven monitoring, the challenges of data volume and variety, and the future trajectory of autonomous infrastructure management. Ultimately, the synergy between AI and telemetry represents a paradigm shift, transforming infrastructure from a passive foundation into a self-aware, self-healing asset.