Menstrual cycle tracking and ovulation prediction are vital steps in a woman’s reproductive health and family planning. This research paper studies the advancement of menstrual cycle and ovulation prediction using machine learning techniques. We are using a large dataset with 17,985 records and has 117 features, which include estimated ovulation, luteal phase, tracking factors and various factors related to intercourse. Through data preprocessing, we address duplicated data, missing values, outliers, and feature engineering to optimize the dataset for predictive modeling. Decision trees, support vector machines, and neural networks are some of the many machine learning algorithms. Still, we are using random forest and linear mixed models, assessed for their predictive accuracy and generalization capabilities, which are key factors in improving its usability. These results significantly improve menstrual cycle and ovulation prediction compared to conventional calendar-based methods. They provide enhanced accuracy, precision, and external validation to ensure the model is reliable and adapts to people from different parts of the world. The potential applications extend to fertility management, contraception, and healthcare interventions, which enable individuals to make informed reproductive health decisions. In summary, the potential of machine learning in advancing the menstrual cycle and ovulation prediction offers a reliable tool for reproductive health and family planning. More research and clinical validation are essential for implementing such predictive models in real-world health care.