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Abstract Background 1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites. Objective Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated. Methodology The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models. Results The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics. Conclusion For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .