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：The differential expression of pyroptosis-related genes（PRG）between PCOS patients and normal healthy women was analyzed by using three microRNA（mRNA）expression profiles obtained from the Gene Expression Omnibus（GEO）database. 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 PRG to accurately predict PCOS. Among the four machine learning algorithms，the XGB method demonstrated the highest accuracy in validating the performance of the model，which was further supported by decision curve analysis. Consensus clustering revealed two distinct subgroups within PCOS cases，with Cluster 2 exhibiting higher level of immune infiltration compared with Cluster 1. Differential expression analysis was then conducted to identify differentially expressed genes between the two subtypes，followed by pathway enrichment analysis on the genes. Clinical verification showed that the plasma expression levels of the apoptosis associated speck like protein containing a CARD（also named PYD and CARD domain containing，PYCARD），the absent in melanoma 2（AIM2）， the chromatin modif - yingprotein 4B（CHMP4B）and the NOD - like receptor family pyrin domain containing 2（NLRP2）were significantly higher in the PCOS patients than the healthy controls. This verifies the accuracy of the PCOS prediction model based on PRG. Conclusion：This study may offer preliminary insights into the correlation between PCOS and pyroptosis，and provide a precise predictive model for PCOS.