A multi-view deep learning model successfully identified left ventricular outflow tract obstruction in hypertrophic cardiomyopathy patients using only standard 2D echocardiographic images, achieving 84% accuracy (AUROC) in external validation across 1,833 training cases and 46 Korean patients. The AI system eliminated the need for technically demanding Doppler measurements by analyzing spatial-temporal patterns across three cardiac imaging views. This breakthrough could democratize cardiac obstruction screening in resource-limited settings where Doppler expertise is scarce. The technology addresses a critical gap in hypertrophic cardiomyopathy management, where accurate gradient assessment determines treatment decisions including surgical interventions. While promising for portable cardiac screening and real-time clinical decision support, the approach faces typical AI limitations including potential bias from training data and the need for diverse population validation. The external validation on Korean patients suggests reasonable generalizability, though broader demographic testing remains essential. As this research appears as a preprint on medRxiv awaiting peer review, the findings require independent validation before clinical implementation. The work represents an incremental but meaningful advance in cardiac AI, potentially expanding access to specialized cardiac assessment in underserved healthcare environments.