Purpose To develop a machine learning-based model to identify patients with hypertrophic cardiomyopathy (HCM) at high risk of major adverse cardiac events (MACEs) and key predictors of model performance. Materials and Methods This retrospective cohort study included patients who underwent cardiac MRI for HCM evaluation between September 2015 and December 2022. Cardiac MRI included balanced cine steady-state free precession, native T1 and T2 mapping, and late gadolinium enhancement. MACEs were defined as a composite of cardiovascular death, resuscitated sudden cardiac death, or heart failure hospitalization. A penalized Cox proportional hazards model with elastic net regularization (CoxNet) was developed with 33 clinical, genetic, echocardiography, and cardiac MRI variables. Model training used 200 iterations of stratified subsampling cross-validation (80% training, 20% testing). Performance was evaluated with the Harrell C index and compared with the 2014 European Society of Cardiology sudden cardiac death risk model. Results A total of 604 patients were included (mean age, 52 years ± 15 [SD]; 417 male patients; median follow-up, 3.0 years [IQR, 1.9-4.2 years]). The CoxNet model demonstrated favorable performance for MACE prediction (C index, 0.75; 95% CI: 0.65, 0.83), similar to the European Society of Cardiology model (C index, 0.67; 95% CI: 0.57, 0.75; P = .07). Key predictors included apical aneurysm, left ventricular end-systolic volume indexed to body surface area, extensive late gadolinium enhancement, native T1 z score, and male sex. Conclusion A machine learning-based model comprising routinely available variables showed strong performance for MACE prediction in HCM. Key variables highlight the impact of cardiac MRI features on risk stratification. Keywords: MRI, Machine Learning, Model Training, Cardiac, Heart, Hyperplasia, Hypertrophy © RSNA, 2026 See also commentary by Shiwani in this issue.