Glutarylation, which is a newly identified posttranslational modification that occurs on lysine residues, has recently emerged as an important regulator of several metabolic and mitochondrial processes. However, the specific sites of modification on individual proteins, as well as the extent of glutarylation throughout the proteome, remain largely uncharacterized. Though informative, proteomic approaches based on mass spectrometry can be expensive, technically challenging and time-consuming. Therefore, the ability to predict glutarylation sites from protein primary sequences can complement proteomics analyses and help researchers study the characteristics and functional consequences of glutarylation. To this end, we used Random Forest (RF) machine learning strategies to identify the physiochemical and sequence-based features that correlated most substantially with glutarylation. We then used these features to develop a novel method to predict glutarylation sites from primary amino acid sequences using RF. Based on 10-fold cross-validation, the resulting algorithm, termed ‘RF-GlutarySite’, achieved efficiency scores of 75%, 81%, 68% and 0.50 with respect to accuracy (ACC), sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC), respectively. Likewise, using an independent test set, RF-GlutarySite exhibited ACC, SN, SP and MCC scores of 72%, 73%, 70% and 0.43, respectively. Results using both 10-fold cross validation and an independent test set were on par with or better than those achieved by existing glutarylation site predictors. Notably, RF-GlutarySite achieved the highest SN score among available glutarylation site prediction tools. Consequently, our method has the potential to uncover new glutarylation sites and to facilitate the discovery of relationships between glutarylation and well-known lysine modifications, such as acetylation, methylation and SUMOylation, as well as a number of recently identified lysine modifications, such as malonylation and succinylation.