Polycystic ovary syndrome (PCOS) is a common hormonal disorder among women of reproductive age, affecting around 5 million women worldwide. To categorize PCOS based on its characteristics, diverse machine learning methods were utilized, such as the Naïve Bayes classifier, logistic regression, K-nearest neighbor (KNN), classification and regression trees (CART), random forest classifier, and support vector machine (SVM). These methodologies were executed using the Spyder Python IDE. Identifying PCOS can pose difficulties because of the wide array of symptoms and potential overlap with other gynecological conditions. Popular diagnostic methods, which include clinical evaluations, hormone screenings, and ovarian ultrasound scans, may prove time-consuming and financially burdensome for patients. To tackle these obstacles, this work is implementing a framework for the timely detection and anticipation of PCOS utilizing minimal yet promising clinical and metabolic indicators. This framework endeavors to pinpoint crucial characteristics that could serve as preliminary indicators for PCOS, enabling a more streamlined and economically viable diagnostic process. The research gathered information from 541 women during medical consultations and clinical evaluations. Out of the original dataset, 23 attributes derived from clinical and metabolic tests were scrutinized using statistical software (SPSS V 22.0) to isolate 8 promising features based on their statistical significance. Before classification, the feature set underwent transformation via principal component analysis (PCA) to enhance efficiency and mitigate computational complexity. Overall, this work highlights the potential of machine learning techniques in improving the early detection and prediction of PCOS, thereby reducing the burden on patients and healthcare providers associated with traditional diagnostic methods. Additionally, it underscores the importance of identifying and utilizing optimal clinical and metabolic parameters for more accurate diagnosis and management of PCOS.