Women's morality has grown recently as a result of gynecological cancer diagnoses. It is mostly the outcome of a delayed diagnosis caused by a delayed discovery of the illness. Ovarian cancer is typically discovered late and frequently has peritoneal and distant metastases at diagnosis since there aren't many distinct signs and symptoms in the early stages of the illness. Detecting this type of cancer early is crucial for improving survival rates. However, it poses a significant challenge. Early disease detection may be feasible by the use of biomarker data, such as proteomic, genomic, and other molecular data. To anticipate the risk of ovarian cancer and analyze biomarker data, machine learning techniques have become a prominent tool. These methods are gaining greater recognition for their ability to effectively assess biomarker data and support ovarian cancer risk prediction. In this work, we employed various algorithms like logistic regression, support vector machine (SVM), k-nearest neighbor (KNN), random forest, and decision trees. Among these approaches, the random forest classifier has shown promising results, demonstrating a high level of accuracy in predicting ovarian cancer risk. This advancement in predictive modeling holds significant promise for improving patient outcomes by enabling early detection and the implementation of personalized treatment strategies. Ultimately, such efforts contribute to enhancing overall healthcare practices and the well-being of women affected by gynecological cancer.