The investigation of metabolically liable sites of xenobiotics mediated by UDP-glucuronosyltransferases (UGTs) is important for lead optimization in early drug discovery. However, it is time-consuming and costly to identify potentially susceptible sites experimentally. Hence, in silico approaches have been developed to predict the site of metabolism (SOM) of UGT-catalyzed substrates. In the present work, four major types of reactions catalyzed by UGTs were collected from the Handbook of Metabolic Pathways of Xenobiotics along with their corresponding glucuronidation products. These observed and nonobserved SOMs were identified and encoded by atom environment fingerprints. Four resampling methods were performed to treat data imbalance, while four feature selection methods were utilized to choose appropriate features. Three tree-form machine learning algorithms were conducted to build discriminating models, and optimal models were then obtained for the different types of reaction. The results indicated that all of the chosen best models showed area under the curve values ranging from 0.713 to 0.869 for two independent external validation sets. Our study could supply valuable information for optimization of pharmacokinetic profiles and contribute to metabolism prediction.