Objective To investigate the core targets and mechanisms by which isorhamnetin prevents and ameliorates liver fibrosis. Methods Bioinformatic data were integrated to identify liver fibrosis-related targets via differential gene analysis and weighted gene co-expression network analysis (WGCNA). These targets were compared with those that mediate isorhamnetin’s action to identify common targets. Machine learning optimized core targets that were validated for causal association using Mendelian randomization. Molecular docking and dynamics simulations assessed target function. ResultsWe identified 113 interactive targets of liver fibrosis and isorhamnetin, which were primarily enriched in phosphatidylinositol 3 kinase-protein kinase B(PI3K-AKT), tumor necrosis factor(TNF), and other signaling pathways. Machine learning combined with Mendelian randomization pinpointed aryl hydrocarbon receptor(AHR), caspase3(CASP3), and mitogen-activated protein kinase 14(MAPK14) as core targets. Multidataset validation confirmed their consistent expression and significant diagnostic efficacy ( area under the curve >0. 7). Molecular simulations demonstrated stable binding of isorhamnetin to these targets (binding energy<-7. 0 kcal /mol). Conclusions Isorhamnetin inhibits liver fibrosis by targeting AHR, CASP3, and MAPK14 to regulate inflammation, apoptosis, and metabolic pathways. This study provides novel insights into the anti-fibrotic mechanisms of traditional Chinese medicine components.