The 3D ground reaction force（GRF） is an important measurement parameter in human motion analysis， but its measurement is limited. The application status of machine-learning in predicting GRF is systematically reviewed. Searching with the combination of “ground reaction force”，“machine learning”and“neural network as the keyword， fourteen studies of predicting GRF by using machine learning model are screened. The motion tasks in these studies include walking， running and several special tasks in sports. Different learning algorithms are used to predict the GRF， whose input parameters include plantar pressure parameters， human motion parameters obtained by motion capture. The relative root mean square and cross-correlation coefficient are adopted as evaluation indicators. The results show that using the machine learning to predict GRF can obtain excellent prediction accuracy. The application of predicting GRF by using machine learning models in human motion needs more research， such as increasing the sizes of datasets for machine learning to further improve the prediction performance of learning models.