BACKGROUND: Women with polycystic ovary syndrome (PCOS) are prone to excessive ovarian response and ovarian hyperstimulation syndrome (OHSS) during controlled ovarian stimulation, whereas overly conservative gonadotropin (Gn) dosing reduces oocyte yield and efficiency. This study aimed to develop a target-driven machine learning model for estimating individualized Gn starting doses in women with PCOS. METHODS: This retrospective, two-center study analyzed 976 PCOS women undergoing in vitro fertilization-embryo transfer using GnRH-antagonist protocols. Patients were categorized by oocyte yield ( 16). Ovarian sensitivity (OS), defined as retrieved oocytes per unit of Gn starting dose, was modeled as a continuous response metric linking baseline characteristics with Gn requirements. Data were partitioned into training, validation, and test sets (60:20:20). Parallel random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression identified core predictors within the training set. Model accuracy was evaluated via R2, RMSE, and MAE. RESULTS: The 7-16 oocyte-yield category showed a more favorable balance of reproductive outcomes, OHSS risk, time to live birth, and costs. Body mass index (BMI), anti-Müllerian hormone (AMH), basal follicle-stimulating hormone (bFSH), and antral follicle count (AFC) were retained as final predictors. The RF model robustly predicted continuous OS in the independent test set, achieving an R2 of 0.860 (95% CI: 0.809, 0.901), an RMSE of 0.254 (95% CI: 0.218, 0.296), and an MAE of 0.141 (95% CI: 0.127, 0.156). Post-hoc dose-concordance analysis in the 7-16 category showed model-derived doses correlated with historical prescriptions. Simulated dose-adjustment analyses generated upward recommendations for 16 oocytes. An online "Gn Calculator" was developed for clinical decision support. CONCLUSIONS: By predicting observed OS via BMI, AMH, bFSH, and AFC, this target-driven RF framework calculates individualized Gn starting doses for women with PCOS. The Gn Calculator facilitates clinical application and may help guide patients toward optimal outcomes by targeting an ideal oocyte yield.