This study developed a model that combines clinical and genetic data to predict the number of MII oocytes retrieved during ovarian stimulation. Using advanced machine learning on large clinical and smaller clinical-genetic datasets, the combined model outperformed one based on clinical data alone. Key predictors included hormone levels, follicle count, and genetic variants in several reproductive genes. Incorporating genetic information improved prediction accuracy, helping tailor IVF treatment more precisely.