Introduction Managing airworthiness in aging military fleets and executing Service Life Extension Programs (SLEP) requires a shift from document-centric engineering to data-centric Digital Twin methodologies. Airbus Defence & Space's Fatigue Digital Equivalent (FDE) simulates structural degradation using physics-based models and "Degradation Assessment Bots" (DABs). While effective for condition monitoring, its evolution to a predictive "Fatigue Digital Brother" faces scalability issues due to high computational costs. This paper proposes integrating a certifiable Machine Learning (ML) pipeline to enable real-time Condition-Based Maintenance (CBM). International Recognition: The FDE architecture has gained international recognition. The USAF's A-10 Aircraft Structural Integrity Program (ASIP) references the FDE's roadmap, and Chinese researchers cite it as a leading methodology. Our proposal builds on this established architecture, replacing computationally expensive solvers with an ML pipeline to enable predictive capabilities. Integration Strategy: The ML pipeline integrates Deep Learning models into the FDE's "virtual space." It operates in three phases: 1. Stress Vector Prediction: A Deep Neural Network (DNN) approximates the mapping between mission parameters and local stress vectors. 2. Cycle Characterization: Neural networks predict damage contribution from specific load cycles, maintaining physical interpretability. 3. Life Estimation: Outputs are aggregated using Miner’s rule to compute accumulated damage and Remaining Useful Life (RUL). Validation for Certification To gain acceptance from airworthiness authorities, the ML components undergo rigorous statistical validation. This includes ensuring data sufficiency and providing probabilistic outputs with a 95% prediction interval. Evolution and Results This integration represent a leap from Wave 1 (FDE+) to Wave 3 (Fatigue Digital Brother). Experimental results using real fleet data show >95% prediction accuracy compared to high-fidelity physics models, with significantly reduced computational time. The system successfully identifies outlier mission profiles, demonstrating its ability to flag risks missed by static "Digital Relatives." Conclusion Embedding a physically interpretable, statistically validated ML pipeline into the FDE ecosystem solves the scalability challenge of aerospace Digital Twins. This evolution enables fleet-wide, flight-by-flight prognosis, bridging the gap between theoretica