A deep learning algorithm demonstrated superior diagnostic and prognostic performance compared to established cardiology guidelines when assessing diastolic heart function across 5,663 patients from three cohorts. The AI model achieved a C-index of 0.676 versus 0.638 and 0.602 for the 2016 and 2025 American Society of Echocardiography guidelines respectively in predicting cardiovascular outcomes. This artificial intelligence approach represents a significant advancement in cardiovascular medicine, addressing a critical clinical challenge where current guideline-based assessments often rely on complex parameters not consistently available in routine practice. The superior performance was particularly notable in patients with preserved ejection fraction, a population where diastolic dysfunction is notoriously difficult to diagnose yet increasingly recognized as a major cause of heart failure. The AI model's reliance on routinely available echocardiographic variables could democratize accurate cardiac assessment across healthcare settings with varying resource levels. However, this remains a preprint study awaiting peer review, and the findings require validation in diverse clinical populations before implementation. The research suggests a paradigm shift toward AI-assisted cardiac diagnostics, potentially improving early detection of heart failure risk in millions of patients worldwide.