A Canadian collaborative is developing an artificial intelligence model to identify diastolic heart failure in pediatric and young adult patients with primary cardiomyopathy, a condition that frequently goes undiagnosed until advanced stages. The AID-HF initiative will integrate deep cardiac phenotyping with genomics, lipidomics, and proteomics across three major biobank registries, applying machine learning to uncover biological signatures driving cardiac dysfunction. This represents a significant shift toward precision medicine in cardiology, where early detection could dramatically improve outcomes for young patients whose heart failure stems from inherited muscle diseases rather than acquired conditions like diabetes or hypertension. The multiomics approach may reveal novel therapeutic targets and biomarkers that could transform how clinicians monitor at-risk youth. However, as this is a study protocol preprint awaiting peer review, the actual performance and clinical utility of the AI model remain to be demonstrated. The initiative's success will depend on the quality of data integration across registries and whether machine learning can truly identify actionable patterns in this complex disease landscape.