A deep learning system achieved remarkable diagnostic accuracy across five mitral valve conditions, with area under the curve values ranging from 0.931 to 0.997 when analyzing routine echocardiogram images from 6,606 patients across multiple medical centers. The AI correctly identified normal valves, rheumatic disease, degenerative changes, valve prolapse, and functional disorders with sensitivity that improved significantly when moderate or severe regurgitation was present. This breakthrough addresses a critical clinical challenge in cardiology, where accurate mitral valve diagnosis traditionally requires extensive expertise and subjective interpretation. The technology could democratize advanced cardiac care by enabling consistent, expert-level valve assessment in routine clinical settings, particularly valuable in underserved areas lacking specialized cardiologists. The model's robust performance across different image qualities and disease severities suggests real-world applicability. However, important limitations include the model's lower sensitivity for degenerative disease and the complex nature of cases with multiple overlapping etiologies. As this is a preprint awaiting peer review, these promising results require validation through the formal publication process before clinical implementation can be considered.