Neural Architecture Search identified optimal deep learning architectures that achieved 97% accuracy in classifying cardiac arrhythmias from 88,253 electrocardiograms across three continents. The automated approach converged on surprisingly minimal configurations—just 384 embedding dimensions, 4 depth layers, and 4 self-attention heads—while outperforming the 2021 PhysioNet Challenge winner (0.71 vs 0.63 challenge metric). This finding challenges assumptions that complex architectures are necessary for sophisticated medical AI tasks. The streamlined design has profound implications for cardiovascular monitoring, as sudden cardiac death claims roughly 50% of all cardiac fatalities and often strikes without warning. Lightweight computational requirements could enable real-time arrhythmia detection in wearable devices, potentially transforming prevention strategies in everyday environments. However, the model struggled with rare conditions like Low-QRS Voltage (89% accuracy), highlighting persistent challenges with class imbalance in medical AI. The self-attention mechanism's ability to autonomously extract clinical features could simplify deployment by eliminating manual feature engineering. As this is a preprint awaiting peer review, these promising results require validation through independent replication and clinical testing before implementation in patient care.