Abstract: Objective: To investigate the mechanism and core targets of isorhamnetin in preventing and treating liver fibrosis. Methods: Bioinformatics data were integrated to identify liver fibrosis-related targets via differential gene analysis and weighted gene co-expression network analysis (WGCNA). Key intersecting targets with isorhamnetin's action targets were screened. Machine learning optimized core targets, validated for causal association using Mendelian randomization (MR). Molecular docking and dynamics simulations assessed target function. Results: 113 interactive targets of liver fibrosis and isorhamnetin were identified, primarily enriched in PI3K-AKT, TNF, and other signaling pathways. Machine learning combined with MR pinpointed AHR, CASP3, and MAPK14 as core targets. Multi-dataset validation confirmed their consistent expression and significant diagnostic efficacy (AUC >0.7). Molecular simulations demonstrated stable binding of isorhamnetin to these targets (binding energy < -7.0 kcal/mol). Conclusion: 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.