Autonomic nervous system pathology manifests early in Parkinson’s disease (PD) course. Although heart rate variability measured by a 5-minute electrocardiogram (ECG) is reported to be reduced in PD, little is known about ECG markers during prodromal stage, and brief 10-second ECGs have been rarely studied. The aim of the study is to build externally validated ECG based fully automatic artificial intelligence (AI) model to predict PD risk during prodromal stage. We obtained data for PD and date/age/sex/race-matched control subjects from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). We used AI to predict PD risk from standard 10-second 12-lead ECGs performed between 6-months-5 years before diagnosis. The prediction model was built with MLH data and externally validated with LUC data. We identified 131 cases/1058 controls at MLH and 29 cases/165 controls at LUC. We initially trained models on 90% of the MLH data and internally validated them in the remaining 10%. The best performing model with an internal validation was a convolutional neural network model. External validation with LUC data yielded an AUC of 0.67. When we considered only ECGs obtained 6-months to 1-year preceding PD diagnosis, the external validation AUC was 0.74. Using only ECG, we built a predictive model that correctly classified individuals with prodromal PD with moderate accuracy. The model was effective in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help to identify individuals with prodromal PD for inclusion in disease-modifying therapeutic trials.