RNA modification, as a crucial post-transcriptional regulatory mechanism, plays a pivotal role in normal physiological processes and is closely associated with the onset and progression of various human diseases. Recent studies have highlighted significant alterations in the level of RNA modifications, including m6A, m6Am, m1A, m5C, m7G, ac4C, Ψ, and A-to-I editing, across multiple diseases. These findings suggest the potential of RNA modifications and their regulatory factors as biomarkers for early disease diagnosis and prognosis. This review provides an overview of statistical methods, machine learning techniques employed in identifying disease diagnostic and prognostic biomarkers, along with relevant evaluation metrics and bioinformatics tools. We further explore the types of common RNA modifications, the modifying proteins involved, and the underlying mechanisms of modification. The focus of this paper is on the application of machine learning algorithms in discovering RNA modification-related biomarkers, particularly for disease diagnosis and prognosis. By reviewing recent advancements in the identification of disease biomarkers, and analyzing the prospects and challenges of their clinical application, we aim to offer insights into the mining methods of RNA modifications and their associated factors as disease diagnostic or prognostic biomarkers, providing a valuable reference for future research and clinical practice.