We present RegioML, an atom-based machine learning model for predicting the regioselectivities of electrophilic aromatic substitution (EAS) reactions. The model relies on ultra fast quantum chemical descriptor calculations combined with an ensemble decision tree variant called light gradient boosting machine (LightGBM). The atomic descriptors are based on CM5 atomic charges obtained from a single conformer embedding with RDKit 28 and a single point calculation using the open source semiempirical tight binding method GFN1-xTB. The model is trained and tested on 21,201 bromination EAS reactions and 101K reaction centers, which is split into a training, test, and out-of-sample datasets with 58K, 15K, and 27K reaction centers, respectively. The accuracy is 93% and 90% for the test and out-of-sample set, respectively, but this is not a good measure of performance due to the preponderance of non-reactive sites. For example, the precision (the percentage of positive predictions that are correct) is 88% for the test set, but only 80% for the out-of-sample set.