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2- O -methylation (2OM) is a vital post-transcriptional modification which is formed by a functional group through the attachment of a methyl (–CH3) group to the second position of an aromatic ring hydroxyl group (–OH). It plays an active part in RNA physical configuration stability and the way different RNA molecules interrelate. Further, this modification plays a pivotal role in changing the epigenetic regulation of cellular processes. Previous approaches like mass spectrometry could not fully enhance the identification of RNA-modified sites. Sequence data were useful in the development of measures that meant the use of computationally intelligent system to identify 2OM sites quickly. This research proposed a new novel method of feature extraction and generation from the available sequences, and the feature dimensionality reduction has been done through the incorporation of statistical moments. The final feature vectors were developed and used to train prediction models. The assessment of prediction models was carried out through independent set tests and  k -fold cross-validation. Through rigorous testing, the bagging ensemble model outperformed and revealed optimal accuracy scores. A publicly accessible web-based application has been developed which can be accessed via  https://2om-pred-webapp.streamlit.app/ .

2OM-Pred: prediction of 2- O -methylation sites in ribonucleic acid using diverse classifiers