5-methylcytosine (m ⁵C) is a widely known epigenetic moderation in RNA types. Methyltransferases catalyze the genesis of m5C. This site of RNA plays a crucial role in many biological activities. For many years in DNA, the synthetic process and biological role of m ⁵C sites have remained the concentrating domain for researchers. Recently, many characters of RNA m ⁵C sites have been discovered, but it is still considered in their infancy. The accurate and systematic detection and classification of m ⁵C remains a challenging task. The existence of m ⁵C sites shows a thriving role in numerous organic activities. Machine learning techniques are alternatives to laboratory experiments, which will ease the m ⁵C site’s identification in Homo sapiens. This article presents a novel computational model named m ⁵C-TNkmer to extract RNA sequences. The model is enriched with the k-mer feature extraction technique. Four subdatasets of the primary data set are created: DNC, TNC, tetra-NC, and penta-NC. The results highlighted that m ⁵C-TNKmers achieved 96.15% accuracy. The suggested technique is a talented one that will help scientists correctly identify RNA m ⁵C sites and their modification. It provides a clue to better understanding genetic function and controlling roles.