<p>This study presents a comprehensive methodology for predicting the fatigue life of an unmanned aerial vehicle (UAV) by integrating real-time flight data with finite element analysis (FEA). The approach utilizes load factors (<i>N</i>_<i>z</i>) extracted from the UAV’s flight data recorder (FDR), which are filtered using level count crossing and rain flow cycle-counting techniques to generate a detailed load spectrum. This spectrum forms the basis for calculating lift distributions using Schrenk’s approximation method, which is subsequently employed in static structural analysis to identify fatigue-critical locations (FCLs). The methodology assesses the fatigue life by determining the number of load cycles and the corresponding cycles to failure for materials at FCLs, using S–N curves. The cumulative damage is evaluated through the Palmgren–Miner rule, leading to the estimation of the safe fatigue life using the safe-life design approach. To enhance the accuracy of predictions, the S–N curves are adjusted to account for surface roughness and reliability factors. The proposed framework, validated against existing literature, is applied to critical regions of the UAV wing, specifically the main spar and rib sections. Refinements in fatigue life predictions are achieved by incorporating scatter factors, ensuring improved precision and realism in the analysis. This research establishes a physics-based digital twin framework that integrates high-frequency FDR telemetry with high-fidelity FEA, enabling a quasi-real-time diagnostic tool for predicting the remaining useful life (RUL) of UAVs as a high-accuracy alternative to cost-prohibitive full-scale fatigue testing.</p>