The article is dedicated to the analysis of automated monitoring and self-healing mechanisms in high-availability cloud databases operating under distributed, multilayer architectures. The relevance of the study is determined by the growing structural complexity of cloud native database clusters, where traditional threshold driven alerting fails to capture compound and metastable failure dynamics. The scientific novelty lies in the integrated interpretation of Graph-based anomaly localization, LLM-assisted diagnostic reasoning, adaptive concept drift detection, multivariate monitoring, and self-healing orchestration across layers as components of a unified distributed control regime. The work describes architectural solutions for topology-aware monitoring, recursive diagnosis, speculative recovery, and multi-cloud failover coordination. Special attention is paid to metastable instability and feedback amplification risks in autonomous remediation systems. The goal of the study is to systematize methodological approaches and identify structural regularities shaping resilient database infrastructures. Analytical synthesis, comparative source analysis, and structural modeling were used to achieve this goal. The conclusion demonstrates that availability emerges as a managed continuum formed by coordinated interpretive and corrective loops. The article will be useful for database architects, cloud engineers, and researchers in intelligent infrastructure systems.