Ulcerative colitis, a chronic inflammatory condition of the large intestine, is commonly observed in older people. Embelin, derived from E. ribes, has anti-inflammatory and anticancer properties and is part of the traditional Vaibhang treatment for ulcerative colitis. However, the specific mechanism and effect on gene targets in ulcerative colitis are not fully understood. This study used WGCNA, differential gene expression analysis, and machine learning to identify gene targets involved in the pathogenesis of UC and explore the therapeutic benefits of embelin. The Random Forest algorithm was used to examine gene expression patterns and to understand how embelin affects biological pathways relevant to the condition. Additionally, molecular docking simulations were conducted to investigate the molecular mechanisms by which embelin prevents ulcerative colitis. WGCNA identified brown and purple modules, comprising 574 and 98 gene targets, respectively, that were linked to ulcerative colitis. Differential gene expression analysis revealed 1,470 genes, of which 986 were upregulated and 484 were downregulated. NLRP3 was identified as a prominent diagnostic biomarker for this disease. Functional enrichment analysis highlighted the significance of NLRP3 in biological processes, pathways, and immune responses associated with ulcerative colitis. Molecular docking simulation of embelin with NLRP3 confirmed the better interaction with a binding energy of (-7.9 kcal/mol) forming three hydrogen bonds, providing structural evidence for the development of embelin-based drugs to treat UC. This study demonstrated the molecular mechanisms of genes involved in the pathogenesis of UC and revealed the potential therapeutic and diagnostic biomarker NLRP3 in ulcerative colitis.