In the contemporary landscape of software engineering, the transition from monolithic architectures to highly distributed microservices has introduced unprecedented complexity. Traditional monitoring techniques, which rely on static thresholds and reactive troubleshooting, are increasingly insufficient for maintaining system health and performance. AI-Powered Observability emerges as a transformative paradigm, leveraging machine learning (ML) and artificial intelligence (AI) to interpret the massive volumes of telemetry data—metrics, logs, and traces—generated by these systems. This article explores the convergence of AI and observability, focusing on how automated anomaly detection, root cause analysis, and predictive analytics provide a proactive approach to system reliability. By shifting from \"knowing what happened\" to \"understanding why it happened,\" AI-powered tools enable engineers to navigate the intricacies of ephemeral infrastructure and asynchronous communication. This review synthesizes current methodologies, the evolution of AIOps, and the future trajectory of intelligent distributed systems, highlighting the critical role of data-driven insights in ensuring high availability and seamless user experiences.