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Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age, characterized by hormonal imbalances, irregular menstruation, and polycystic ovaries. Early and accurate prediction of PCOS is vital for timely intervention and management. Machine learning (ML) algorithms have emerged as powerful tools for predicting PCOS by analyzing complex datasets efficiently. This review focuses on studies from 2014 to 2024 that apply ML techniques to predict PCOS, utilizing databases such as PubMed, Scopus, and Google Scholar. Supervised learning algorithms like Random Forests and Support Vector Machines, deep learning models, and hybrid approaches are commonly employed. These models leverage data such as clinical symptoms, biochemical markers, and ultrasound imaging results to enhance prediction accuracy. Despite promising results, challenges persist, including data imbalance, feature selection, and model interpretability. Opportunities for improvement include integrating multi-omics data, advancing personalized medicine, and developing accessible cloud-based tools. This review highlights the performance metrics of various ML algorithms, underscoring their potential to revolutionize PCOS diagnosis. By integrating ML models into clinical practice, healthcare providers can improve diagnostic efficiency, reduce costs, and offer tailored care to patients, paving the way for more effective PCOS management.