<p>Periodontitis (PD) is a complex oral inflammatory disease with diverse pathogenic factors, demanding effective multi-target therapeutic approaches. Traditional Chinese Medicine (TCM) formulations, like the Kouqiangjie Formula (KQJF), hold potential as alternative therapies due to their multiple pharmacological effects. This study comprehensively investigated the key active ingredients and molecular targets of KQJF in treating PD through a combination of network pharmacology, machine learning, Mendelian randomization (MR), and experimental validation. The active components and targets of KQJF were identified via the TCMSP and HERB databases, while PD-related genes were sourced from GeneCards, CTD, and DisGeNET. Gene expression data from GEO datasets enabled differential expression analysis. Machine learning models, including Random Forest (RF) and Support Vector Machine (SVM), were employed to evaluate the diagnostic potential of gene sets. Molecular docking was utilized to assess the interactions between active ingredients and targets, and MR analysis was conducted to explore the causal relationships with PD. Experimental validation was carried out using a rat model. The results indicated that KQJF consists of 193 active compounds that target 561 proteins, with a significant overlap of 272 targets related to PD. Key compounds such as luteolin, linolenic acid, and naringenin were identified. The SVM model demonstrated excellent predictive performance, with an AUC of 0.954. MR analysis revealed a significant causal effect of the CASP3 gene on the risk of PD (OR = 1.595, <i>p</i> = 0.015). Experimental findings showed that these compounds could reduce the expression of CASP3 and improve the integrity of periodontal tissues. In conclusion, luteolin, linolenic acid, and naringenin are the core compounds in KQJF, and CASP3 is an important target. This study emphasizes the great potential of KQJF for PD treatment and provides a solid data base for the development of new therapeutic strategies.</p>