Funding Acknowledgements Type of funding sources: None. Background Accurate risk stratification in patients with suspected stable coronary artery disease (CAD) is essential for choosing an appropriate treatment strategy but remains challenging in clinical practice. Purpose Our aim was to develop and validate a risk model to predict the presence of obstructive CAD after Rubidium-82 PET and a coronary artery calcium score (CACS) scan using a machine learning (ML) algorithm. Methods We retrospectively included 1007 patients without prior cardiovascular history and  a low-intermediate pre-test likelihood, referred for rest and regadenoson-induced stress Rubidium-82 PET combined with a CACS scan. Multiple features were included in the ML model; PET derived features such as summed difference score and flow values, CACS, cardiovascular risk factors (cigarette smoking, hypertension, hypercholesterolemia, diabetes, positive family history of CAD), medication; age; gender; body mass index; creatinine serum values; and visual PET interpretation. An XGBoost ML algorithm was developed using a subset of 805 patients to predict obstructive CAD by using 5-fold cross validation in combination with a grid search. Obstructive CAD during follow-up was defined as a significant stenosis during invasive coronary angiography, a percutaneous coronary intervention or a coronary artery bypass graft procedure. The ML algorithm was validated with unseen data of the remaining 202 patients. Results Application of the XGBoost algorithm resulted in an area under the curve (AUC) of 0.93 using the training data (n = 805) and an AUC of 0.89 using the unseen data (n = 202) in predicting obstructive CAD. The strongest predictors were the CAC-scores and quantitative PET derived features. The classical risk factors and medication hardly provided an added value in the prediction of obstructive CAD. Conclusion The developed ML algorithm is able to provide individualized risk stratification by predicting the probability of obstructive CAD.  Although validation with a larger dataset could result in a more well defined performance range, this model already shows potential to be implemented in the diagnostic workflow.