Stressor-associated atrial fibrillation (AF) refers to new-onset AF that occurs with a reversible, acute stressor. Identifying individuals at highest risk for AF recurrence is essential to guide management. Although clinical factors have shown limited value, the utility of contemporary artificial intelligence (AI)-enabled models using the 12-lead ECG to estimate recurrence risk remains unknown. We retrospectively analyzed consecutive primary care and cardiology patients with stressor-associated AF occurring during hospitalization. We quantified the cumulative incidence of recurrence accounting for death as a competing risk. We investigated the relationship between time-varying recurrence and a composite end point of AF-related adverse events (stroke, heart failure, all-cause death) using Cox models. We then developed and validated a penalized regression model to predict recurrence using clinical factors, stressor type, and AF risk estimates from a previously validated ECG-based AI model. We analyzed 3371 patients with stressor-associated AF (mean age, 69±12 years; 40% women). Over a median of 3.7 years (interquartile range, 1.8-7.2), the 10-year cumulative incidence of AF recurrence was 41% (95% CI, 39-44). AF recurrence was strongly associated with AF-related adverse events (hazard ratio, 2.24 [95% CI, 1.81-2.76]). A model incorporating clinical factors, stressor type, and ECG-based AI model AF risk estimates (clinical-AI) discriminated AF recurrence (area under the receiver operating characteristic curve, 0.768 [95% CI, 0.707-0.830]) favorably compared with clinical features (area under the receiver operating characteristic curve, 0.707 [95% CI, 0.642-0.772]; AF recurrence rates following stressor-associated AF are considerable and are associated with substantially higher risk of adverse cardiovascular events. Models incorporating ECG-based AI risk estimates may prioritize individuals for intensive monitoring and preventive interventions.