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The accurate prediction of fertility outcomes is an extremely interesting and challenging task in reproductive medicine. Efforts in this area focus on classic statistical models and newer technologies, including machine learning. The modeling process has three steps, namely, data preparation, model selection and data fitting, and model validation. Here, we present a review of studies on these methods of fertility prediction. Various databases were searched using relevant keywords. Original studies with full-text available on this topic were included for review. Earlier studies explored prediction models for spontaneous pregnancy prognosis, reproductive outcomes after intrauterine insemination and in vitro fertilization, and implantation potential based on embryo morphology and morphokinetic data. Future directions for predictive modeling in reproductive medicine include solving problems presented by big data, identifying novel informative features, balancing predictive power and result interpretability, and validating models with gold-standard study designs.