Background: and Purpose Cardioembolic sources account for 20%–30% of acute ischemic strokes (AIS), often with high morbidity. Conventional imaging confirms etiology retrospectively but lacks insight into the dynamic behavior of embolic transport. We aimed to predict stroke laterality by integrating patient-specific computational fluid dynamics (CFD) simulations with robust Bayesian logistic regression modeling. Methods: Eight patients (median age 77.5 years; 2 females) with anterior circulation AIS of confirmed cardiac origin underwent high-resolution computed tomography angiography. Vascular geometries were segmented to generate CFD models simulating physiologic pulsatile flow. In each cardiac cycle, 1,000 massless particles were released at the aortic inlet. Two features were derived: x1 (long-term embolic bias over 10 seconds) and x2 (short-term embolic bias during the first cardiac cycle). These were used as predictors in a robust Bayesian logistic regression model. Results: The right internal carotid artery (ICA) received more embolic particles (mean 34/s) than the left ICA (mean 28/s). Patients with right-sided strokes had higher x1 (median 0.27 vs. -0.44) and lower x2 (median -0.82 vs. 0.56) than those with left-sided strokes. The model yielded posterior mean coefficients of 1.51 (95% credible interval [CrI]: -0.46 to 4.11) for x1 and -1.96 (95% CrI: -4.88 to 0.20) for x2, achieving complete separation of stroke patients by laterality in this pilot cohort. Conclusions The combination of CFD-based embolic modeling and Bayesian analysis accurately predicted stroke laterality in cardioembolic AIS, exposing distinct patient-specific embolic transport dynamics.