This chapter examines the rising incidence of Polycystic Ovary Syndrome (PCOS), a prevalent hormonal disorder with significant implications for women’s health, particularly in relation to infertility. Early detection of PCOS is essential for effective management and minimizing complications. As machine learning (ML) has demonstrated superior accuracy in feature extraction for diagnostic purposes, various ML models, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), and Naive Bayes (NB), are explored for PCOS detection. This chapter provides an in-depth analysis of ML-based techniques for identifying PCOS, detailing the effectiveness of each approach based on extensive research from recent decades. It reviews datasets commonly used in PCOS studies, offering quantitative and qualitative performance comparisons among algorithms. The chapter concludes by highlighting key challenges in current methodologies and identifying promising directions for future research in ML-based PCOS detection.