Background Postoperative atrial fibrillation (POAF) affects 20 ̶50% of patients undergoing cardiac surgery and is associated with prolonged hospital stays and adverse outcomes. Although several risk factors for developing POAF have been identified, accurate prediction remains challenging. Wearable echocardiography (ECG) patches and remote patient monitoring now enable continuous heart rhythm surveillance, while artificial intelligence (AI) models may detect subtle, yet distinct electrophysiologic signatures that precede POAF development. Objective This study evaluates whether combining continuous ECG patch monitoring with deep learning models can improve both early risk stratification and near real-time prediction of POAF after cardiac surgery. Methods We analyzed continuous ECG and wearable-derived physiology from 101 postoperative cardiac surgery patients enrolled in a prospective remote monitoring trial. Each patient wore an adhesive patch sensor (VivaLNK VV-330) for 14 days after hospital discharge, capturing per-second ECG and activity streams. We developed two complementary deep learning pipelines: (1) a daily-level multimodal Transformer, which downsampled ECG and contextual “TAB tokens” into day-wise units to predict AF occurrence and burden, and (2) an hour-ahead forecasting model, which condensed the last two hours of minute-level physiology into attention-weighted summaries to generate rolling, causal predictions of AF risk in the subsequent hour. Results Across nearly 1.7 million downsampled data elements, the daily-level model showed conservative behavior with very low false negatives, consistently identifying AF-positive days and correctly stratifying high-burden episodes. The hour-ahead forecasting model was trained and validated on 9,267 windows (hours) and achieved excellent discrimination (AUC 0.945), high specificity (0.99), and strong predictive value (NPV 0.98). Recall-oriented calibration further reduced missed AF hours while maintaining low false alarms. Together, these frameworks provided reliable daily burden stratification and fine-grained, near real-time risk forecasting. Conclusion Continuous multimodal monitoring paired with AI enables accurate POAF detection, daily risk stratification, and rolling hour-ahead forecasts. This dual-resolution framework has the potential to support perioperative decision-making by enabling earlier intervention, targeted surveillance, and optimized allocation of preventive therapies in cardiac surgery patients.