Hypertrophic cardiomyopathy (HCM) is a complex genetic cardiovascular disease, with current risk stratification strategies showing limited accuracy in predicting sudden cardiac death and clinical outcomes. This review examines how artificial intelligence (AI) is transforming personalized risk prediction and management in HCM, with particular focus on validated clinical applications. We conducted a comprehensive literature search across PubMed, IEEE Xplore, Web of Science, and Scopus databases from January 2015 to January 2025. Search terms included "artificial intelligence", "machine learning", "deep learning", "hypertrophic cardiomyopathy", and "risk prediction". Inclusion criteria comprised peer-reviewed studies reporting AI applications in HCM with validated performance metrics. We excluded case reports, editorials, and studies without clinical validation. Of 487 identified articles, 84 met inclusion criteria and were analyzed for AI techniques, clinical applications, performance metrics, and implementation challenges. Machine learning algorithms have achieved significant breakthroughs in HCM care. Random forest models identifying ventricular arrhythmias demonstrated 83% accuracy (area under the curve (AUC): 0.83), discovering 12 novel predictors, including left atrial volume index. Deep learning ECG analysis using convolutional neural networks achieved 85-87% accuracy in sudden cardiac death prediction, substantially outperforming traditional risk scores (AUC: 0.87 vs. 0.62). AI-enhanced genetic testing has shown 96% accuracy in reclassifying variants of uncertain significance, while automated cardiac MRI analysis provides objective disease progression monitoring with reduced inter-observer variability. Real-time applications include automated ECG screening tools currently in pilot programs at major cardiac centers, and decision support systems for therapy selection showing >90% accuracy in predicting response to cardiac resynchronization therapy. Multi-center collaborations such as the SHaRe Registry are developing standardized AI models across institutions. Implementation faces specific barriers, including data bias from underrepresented populations, lack of standardized electronic health record formats across centers, regulatory approval pathways for AI-based clinical tools, and "black box" interpretability issues requiring explainable AI solutions. Integration requires addressing these challenges through prospective validation studies, development of regulatory frameworks, and clinician training programs. AI demonstrates transformative potential in HCM management, but realizing clinical benefits requires addressing technical, ethical, and implementation challenges through coordinated multidisciplinary efforts.
