Bone Stress Injuries (BSIs) are overuse injuries caused by repetitive mechanical loading without adequate recovery. Although clinical assessments incorporate training history, demographic factors, and biological characteristics to estimate BSI risk, there is currently no reliable or reproducible method to define individual-specific training thresholds. Fatigue life, defined as the number of loading cycles a bone can sustain before failure, serves as a quantitative surrogate for injury risk in cadaveric models.Finite Element (FE) methods have been validated for estimating bone strain and predicting fatigue life;​ however, their utility is constrained by the time, expertise, and computational resources required for model construction and analysis. This dissertation explores the potential of machine learning (ML) as a more scalable and efficient alternative for predicting bone fatigue life directly from medical imaging data, focusing specifically on the metatarsals, which account for approximately 20% of clinically observed BSIs.The first phase involved subjecting cadaveric metatarsals to physiologically relevant cyclic loading. Many of the bones failed and relationships between FE-derived strain metrics and experimentally measured fatigue life were analyzed to establish a baseline model. In the second phase, ML models were trained using CT images, demographic data, and selected FE-derived features to predict fatigue life. A final model was developed that achieved accurate predictions, ultimatelywithout requiring explicit FE analysis. In the third phase, this model was applied to a dataset of runners to estimate metatarsal fatigue life under varying bone mineral density (BMD) conditions, simulating physiological adaptations to disuse and overuse.This work advances scientific understanding of how bone structure and material properties influence mechanical durability. It also establishes a foundation for in-vivofatigue life estimation, providing a critical step toward personalized training recommendations aimed at mitigating BSI risk in high-load populations.