Rheumatoid arthritis (RA) is a multifaceted autoimmune disease that is marked by a complex molecular profile influenced by an array of factors, including genetic, epigenetic, and environmental elements. Despite significant advancements in research, the precise etiology of RA remains elusive, presenting challenges in developing innovative therapeutic markers. This study takes an integrated multi-omics approach to uncover novel therapeutic markers for RA. By analyzing both transcriptomics and epigenomics datasets, we identified common gene candidates that span these two omics levels in patients diagnosed with RA. Remarkably, we discovered eighteen multi-evidence genes (MEGs) that are prevalent across transcriptomics and epigenomics, twelve of which have not been previously linked directly to RA. The bioinformatics analyses of the twelve novel MEGs revealed they are part of tightly interconnected protein–protein interaction networks directly related to RA-associated KEGG pathways and gene ontology terms. Furthermore, these novel MEGs exhibited direct interactions with miRNAs linked to RA, underscoring their critical role in the disease’s pathogenicity. Overall, this comprehensive bioinformatics approach opens avenues for identifying new candidate markers for RA, empowering researchers to validate these markers efficiently through experimental studies. By advancing our understanding of RA, we can pave the way for more effective therapies and improved patient outcomes.