Polycystic ovarian syndrome (PCOS) is a complex hormonal disorder that affects women of reproductive age and is associated with various metabolic issues, including obesity, high blood pressure, insulin resistance, abnormal cholesterol levels, and an increased risk of heart disease. Early identification is crucial to enhance long-term health outcomes considering the increased cardiovascular risk in PCOS patients. Improving general well-being depends on quick detection as women with PCOS are more prone to cardiac problems. The current studies on the use of ML methods to evaluate the risk of cardiovascular disease (CVD) in women diagnosed with PCOS are investigated in this review. This work investigated present research using ML techniques like Support Vector Machines (SVM), logistic regression, and Random Forest to project CVD risk in PCOS patients. Furthermore, high-risk disorders like obesity, diabetes, cardiovascular disease, and hypertension might follow from delayed PCOS diagnosis. Examining the potential of ML algorithms for PCOS diagnosis using risk variables such hypertension, diabetes, obesity, and cardiovascular disease was the major objective of this work. Merging algorithms that offer the best accuracy in the creation of hybrid algorithms is another direction of progress. In order to improve long-term health outcomes, this study highlights the need of identifying cardiovascular issues in PCOS patients early on.