Database downtime represents one of the most financially devastating events in modern enterprise computing, with estimated losses ranging from $5,000 to $10,000 per minute depending on organizational scale and industry sector. Conventional backup strategies—whether manual or time-triggered—remain fundamentally reactive, failing to anticipate imminent database failures until data loss has already occurred. This paper presents an AI-Driven Fault-Tolerant Automated Database Backup and Recovery System (AIFDBAR) designed for PostgreSQL deployments in multi-user cloud environments. The proposed architecture integrates a real-time health monitoring subsystem that harvests PostgreSQL internal statistics views, an LSTM-based anomaly detection engine that assigns per-interval risk scores, and a hybrid backup orchestrator that combines schedule-driven incremental snapshots with prediction-triggered urgent backups. Recovery is coordinated by a Point-in-Time Recovery (PITR) orchestrator that targets Amazon S3-compatible object storage and exposes a RESTful management API secured via JSON Web Tokens. Experimental evaluation on an AWS t3.medium instance using the TPC-H benchmark dataset and 100 synthetic failure injections demonstrates a Recovery Time Objective (RTO) of 45 seconds compared to 5 minutes achieved by pgBackRest—an 85% reduction—while maintaining an anomaly detection AUC of 0.94 and a false-positive rate below 4.1%. The system achieves 99.8% modeled uptime availability, offering a compelling path toward fully autonomous database resilience.

Keywords — PostgreSQL, fault tolerance, AI monitoring, automated recovery, anomaly detection