Power transformers are key elements of electrical power systems and, as a result of an internal or external faults, may experience performance loss and major outages. This study establishes a maintenance framework that combines utilizes internal fault analytics with predictive maintenance goals. The framework utilizes data driven diagnostics—including oil analysis and dielectric frequency spectroscopy—along with internal protection analytics. The approach highlights strong correlates between physical measurements and observable protection behavior, under various fault conditions. By emphasizing real diagnostic data and the physical meaning of those measurements, the study presents a firm analytical basis on which to support and strengthen future AI-driven predictive maintenance strategies. The proposed framework integrates traditional fault protection standards with modern data driven maintenance strategies, to create better fault detection, accurate diagnosis, and transformer reliability.