For many years, assisted reproductive technologies (ART) have been helping to conceive a child when this is not possible naturally. We can consider the ART protocol to be successful not only in case of pregnancy, but also in case of its successful completion: the birth of a healthy child. The article describes the creation of a software application for employees of ART centers, which helps to predict the outcome of the protocol, including the probability of pregnancy, the forecast of possible complications during its course, the forecast of the time and method of delivery, and the health group (1-5) of the born child. To create the application, we used data on 854 protocols implemented in 2016-2018, because of which 464 children were born. The analysis of their health contains information from birth to three years of age. The application uses sixteen binary classifiers, nine of which implement multi-class classifications of the term of delivery, the delivery method and children’s health groups. To implement multiclass inference, the “one-vs-all” strategy was used. Сross-validation was used to check the quality. The remaining 7 classifiers predict the likelihood of pregnancy and the occurrence of its complications: cervical incompetence, hypertensive disorders, placenta previa, gestational diabetes mellitus, violations of the amount of amniotic fluid and premature rupture of the membranes. We have built all the models based on the random forest algorithm using the Python programming language. The interface was created using the PyQT5 and QtDesigner libraries.