Infertility is defined as the inability of a couple to have a child within one year after unprotected sex. The prevalence of this phenomenon has affected both patients and society to varying degrees. Accurate prediction of pregnancy outcome is a very meaningful and challenging task in reproductive medicine. In this paper, the modeling methods of assisted reproductive outcome prediction are reviewed and analyzed. The studies found that the modeling process consists of six steps: business understanding, data understanding, data preparation, modeling, model evaluation, and model release. Modeling methods mainly include traditional statistical models and new techniques based on big data(machine learning). Compared with the machine learning predictive model, the traditional predictive modeling method can better explore the action mechanism of each influencing factor on pregnancy outcome, but its predictive ability is relatively weak. Machine learning methods can greatly improve predictive power, but cannot generate transparent and interpretable rules. Therefore, the use of modeling methods should be selected according to different clin. practice purposes. With the development of big data technol., the efficiency of prediction models has been greatly improved. At the same time, there are more challenges, including solving the problems derived from big data, identifying new information features, balancing predictive power and interpretability of results, and researching design and validating models with gold standard research and design.