Objective:To explore the role of genes related to pyroptosis in the pathogenesis of Polycystic ovary syndrome (PCOS) and construct an accurate prediction model for PCOS. Methods: Three micro RNA(mRNA) expression profiles were obtained from the Gene Expression Omnibus (GEO) database ,and analysis was carried out on the differential expression of pyroptosis-related genes (PRGs) between PCOS patients and normal healthy women.Four machine learning algorithms, namely the Generalized Linear Model (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), were employed to identify the gene characteristics of PCOS. Real?time quantitative PCR(RT-qPCR) method was utilized to detect the expression levels of specific genes in the plasma of 10 PCOS patients and 10 normal healthy women.Results: A predictive model and a nomogram were established based on PRGs to accurately predict PCOS. Among the four machine learning algorithms,the XGB method demonstrated the highest accuracy in validating the model using two independent datasets, which was further supported by decision curve analysis. Consensus clustering revealed two distinct subgroups within PCOS cases, with Cluster2 exhibiting higher level of immune infiltration compared to Cluster1. Differential expression analysis was then conducted to identify differentially expressed genes (DEGs) between the two subtypes, followed by pathway enrichment analysis on the model genes. Clinical verification showed that the expression levels of PYD and CARD domain containing(PYCARD), Absent in melanoma 2(AIM2), Chromatin modif-ying protein 4B(CHMP4B) and NOD-like receptor family pyrin domain containing 2(NLRP2) in the plasma of PCOS patients were significantly higher than those of the healthy control group, with statistical differences. This verifies the accuracy of the PCOS prediction model based on PRGs.Conclusion: This study offers preliminary insights into the association between PCOS and pyroptosis,a precise predictive model for PCOS.