Fetal weight is an important indicator of fetal development and maternal safety, but fetal weight cannot be measured directly and can only be predicted according to the examination data of pregnant women. This study proposes a model of fetal weight prediction based on the Genetic Algorithm to optimize BP Neural Network (GA-BPNN). First, the model of continuous weight change in pregnant women is established by using regression model and feature normalization preprocessing. Then, the genetic algorithm is used to optimize the initial weights and thresholds of BP neural network and establish a fetal weight prediction model. 3000 pregnant women data are randomly sampled from a hospital in the eastern part of China in 2016. The proposed model is compared with the prediction model based on the traditional BP neural network. The results show that the GA-BPNN fetal weight prediction model proposed in this paper not only accelerates the convergence of the model, but also improves the prediction accuracy of fetal weight by 14%.