The Indian Air Force (IAF) is deploying Big Data analytics and Machine Learning (ML) algorithms to transition from reactive or scheduled maintenance to Predictive Maintenance for its diverse fleet, including indigenous Tejas fighters and Russian-origin Sukhoi Su-30MKIs. Modern combat aircraft generate gigabytes of telemetry data per flight via onboard Health and Usage Monitoring Systems (HUMS), which log metrics such as engine core temperatures, hydraulic pressure fluctuations, and vibration frequencies. Predictive maintenance uses a Digital Twin, a software replica of a physical aircraft, where ML models compare real-time sensor data against historical baselines. For example, an AL-31FP engine on a Su-30MKI exhibiting a 0.2 per cent increase in turbine vibration with a heat spike triggers an immediate flag, allowing the AI to estimate the remaining useful life of the bearing and queue a replacement before catastrophic failure. The IAF’s fleet diversity, spanning platforms from India, Russia, France, and Western OEMs, requires tracking hundreds of thousands of distinct parts. Big Data analytics merges engine health monitoring with automated inventory databases under digitised logistics frameworks, with the AI platform monitoring depot stock levels across maintenance commands to prevent bottlenecks.