Artificial intelligence applied to standard electrocardiograms successfully predicted mortality risk in 11,513 hospitalized acute heart failure patients, achieving 85% accuracy in identifying severe diastolic dysfunction. The AI-ECG model provided diastolic function grading in 100% of patients, compared to traditional echocardiography which was indeterminate in 44% of cases. Patients with AI-detected Grade 3 dysfunction faced 44% higher mortality risk even after adjusting for other risk factors. This breakthrough transforms ubiquitous ECG technology into a sophisticated cardiac assessment tool. Traditional echocardiographic evaluation of heart failure requires specialized equipment, trained technicians, and optimal imaging conditions—often unavailable in emergency settings or resource-limited environments. AI-ECG democratizes advanced cardiac phenotyping by extracting hidden diastolic patterns from routine 12-lead ECGs available in virtually every clinical setting. The ability to track changes in filling pressure probability over time adds dynamic monitoring capabilities previously impossible with standard ECGs. However, this preprint awaits peer review, and validation in diverse populations remains essential. The technology represents a paradigm shift toward accessible, real-time cardiac risk stratification that could revolutionize heart failure management globally.