Taohe-Chengqi decoction (THCQ), a classical traditional Chinese medicinal (TCM) formula, has been extensively used for treating chronic kidney disease (CKD). However, the biological activity and mechanisms of action of its constituents against renal fibrosis have not yet been investigated thoroughly. This study was aimed at devising an integrated strategy for investigating the bioactivity constituents and possible pharmacological mechanisms of the n-butanol extract of THCQ (NE-THCQ) against renal fibrosis. The n-butanol extract of THCQ was prepared by the solvent extraction method. The components of NE-THCQ were analyzed using UPLC-Q/TOF-MS/MS techniques and applied for screening the active components of NE-THCQ according to their oral bioavailability and drug-likeness index. Then, we speculated the potential molecular mechanisms of NE-THCQ against renal fibrosis through pharmacological network analysis. Based on data mining techniques and topological parameters, gene ontology, and pathway enrichment, we established compound-target (C-T), protein-protein interaction (PPI) and compound-target-pathway (C-T-P) networks by Cytoscape to identify the hub targets and pathways. Finally, the potential molecular mechanisms of NE-THCQ against renal fibrosis, as predicted by the network pharmacology analyses, were validated experimentally in renal tubular epithelial cells (HK-2) in vitro and against unilateral ureteral obstruction models in the rat in vivo. We identified 26 components in NE-THCQ and screened seven bioactive ingredients. A total of 118 consensus potential targets associated with renal fibrosis were identified by the network pharmacology approach. The experimental validation results demonstrated that NE-THCQ might inhibit the inflammatory processes, reduce ECM deposition and reverse EMT via PI3K/AKT/mTOR and HIF-1α/VEGF signaling pathways to exert its effect against renal fibrosis. This study identified the potential ingredients of the NE-THCQ by UPLC-Q/TOF-MS/MS and explained the possible mechanisms of NE-THCQ against renal fibrosis by integrating network pharmacology and experimental validation.