Cardiac arrhythmias manifest as alterations in electrocardiogram (ECG) recordings, where diagnosis relies on both inter-beat rhythm and intra-beat electrophysiological features. Existing ECG analysis in clinical devices predominantly depends on rule-based algorithms with manually tuned parameters, which lack robustness under severe waveform distortions. Meanwhile, the development of automated artificial intelligence (AI) methods is constrained by the high cost of large-scale manual annotation. Here, we propose a weakly supervised framework for ECG waveform boundary localization without human annotations. Multiple rule-based algorithms are leveraged to generate complementary weak labels, which are integrated through joint modeling of data distribution, prior knowledge, and prediction subsets to construct a boundary distribution. Boundary uncertainty is further quantified and incorporated into a distributional clustering scheme to identify high-confidence boundary points. This enables reliable supervision for stable model training and facilitates downstream electrophysiological feature extraction. Training and evaluation on a PhysioNet dataset demonstrate that the proposed method achieves precise waveform boundary localization without manual annotations, providing a practical solution for AI-driven boundary detection on large-scale unlabeled ECG data.