Bisphosphonates are the standard treatment for osteoporosis, reducing fracture risk by inhibiting bone resorption. However, long-term use has been associated with rare but serious adverse events, such as atypical femur fractures (AFF). This project employs machine learning (ML) to predict the occurrence of these adverse events by analyzing clinical data that includes bisphosphonate type, dosage, treatment duration, demographic factors, and patient comorbidities. Using anonymized electronic medical records from 139,376 encounters (24,675 unique patients), 920 AFF cases were identified. Multiple ML algorithms—including K Nearest Neighbors (KNN), Boosted Tree, Decision Tree, and Bootstrap Forest—were trained and validated to identify the most effective model. The KNN model demonstrated the highest predictive performance, accurately distinguishing between patients with and without AFF. In the test set, it achieved near-perfect classification for non-AFF cases and correctly identified 89.7% of AFF cases. These findings highlight the potential of ML to identify patients at elevated risk for adverse events, enabling clinicians to balance therapeutic benefits with long-term safety considerations. By supporting data-driven, individualized treatment decisions, this approach could improve the clinical management of osteoporosis and minimize the risks associated with extended bisphosphonate use.