To identify independent predictors of a successful match to reproductive endocrinology and infertility (REI) fellowships, and to develop and internally validate a prediction model for REI match results. Retrospective cohort study. REI fellowship applications sent to the University of Pennsylvania from 2019 to 2023 (excluding 2020), which represented nearly all REI applicants nationally according to National Resident Matching Program (NRMP) data. Demographics, education, training, and academic achievements. Match result, confirmed through online search and communication with program administrators. Univariate analyses identified variables associated with match, which were then included in multivariable models to identify independent predictors. Bootstrapping was used to assess model discrimination and calibration. The final model was integrated into a web-based tool. Of 286 applications (99.0% of REI applications to the NRMP), 199 (69.6%) resulted in a successful match. In univariate analyses, variables associated with match were younger age, attendance at an allopathic U.S. medical school, USMLE and CREOG scores, residency rank, residency affiliation with a fellowship, research experiences, first-author publications, abstracts/articles in progress, and poster presentations. In the adjusted model, independent predictors of match included residency affiliation with an REI fellowship (aOR 5.43, 2.02-14.64), residency rank (aOR 1.77, 1.25-2.50), USMLE score (aOR 1.05, 1.02-1.08), at least one first-author publication (aOR 2.32, 1.08-4.96), projects in progress (aOR 1.26 (1.02-1.55), and poster presentations (aOR 1.07, 1.00-1.15). Attendance at an international medical school was a negative predictor (aOR 0.32, 0.11-0.88). The model achieved an area under the curve (AUC) of 0.883, with 88.5% sensitivity and 65.8% specificity. A refined model without USMLE scores maintained strong performance (C-statistic 0.85, 0.81-0.91; calibration slope 0.91, 0.72-1.24). Affiliation with an REI fellowship, residency reputation, and research output strongly predicted match success. Gender, race, and ethnicity were not major predictors, yet underrepresentation of certain racial and ethnic groups limited the power to detect potential differences. Our prediction model correctly classified >75% of candidates' match results. These findings may help candidates optimize applications and estimate chances of a successful match into REI fellowship, as well as assist programs in critically reviewing their selection criteria for fellowship match.