In order to obtain soil moisture data at deep soil layers and to complete the missing soil moisture measurements at different depths, machine learning technique is used to perform the soil moisture inversion at multi-layers from surface to root zone. Three machine learning algorithms, support vector machine (SVM), back propagation (BP) neural network and random forest (RF), are applied to construct the inversion models for the train and inversion of soil moisture data at different soil layers, and the meteorological factors, which have high correlation coefficient with the soil moisture, are selected as the input factors based on the principal component analysis (PCA) method in each layer of soil. The major findings are: the simulation results of random forest are more stable and the inversion effect is the best; due to the impact of meteorological variables, the three machine learning models perform best for the inversion of surface soil moisture at the surface soil layer within 0 to 10 cm depth; however, the inversion effect for the soil moisture at the deep zone is gradually reduced with the depth of soil layer; the addition of surface soil moisture and soil temperature at different soil layers as driven factors could improve the inversion capacity of machine learning model for the soil moisture at the deep zone.