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【Background】 Accurate prediction of soil water dynamics requires soil hydraulic parameters and the PedoTransfer Function (PTF) is an indirect method estimating soil hydraulic parameters based on easy-to-measure soil properties. 【Objective】 The purpose of this paper is to compare different machine learning methods as a pedotransfer function to estimate soil hydraulic parameters. 【Method】 We used the UNSODA soil hydraulic property database and compared three machine learning methods: artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN). The hydraulic parameters were described by the van Genuchten formula, and the relationship between its parameters with fractions of sand, silt and clay, as well as soil bulk density was analyzed using the three methods. The accuracy of each method was evaluated using measured water retention curves and saturated hydraulic conductivity from soils with different textures. 【Result】 The SVM model was most accurate and KNN the least to predict soil hydraulic parameters using these easy-to-measure soil properties. Evaluation of operating efficiency of all three methods revealed that the ANN model was least efficient and the KNN the most in model training. In contrast, the ANN model was most efficient while KNN the least in predicting soil hydraulic parameters. Comparison of the three models against the Rosetta model - a commonly used neural-network pedotransfer function with a single hidden layer - found that neural-network models with multiple hidden layers, as used in this paper, were more accurate. We also found that for all three models, increasing the number of input data improved their estimation accuracy. 【Conclusion】 Of the three models, SVM is most accurate for predicting soil hydraulic parameters using fractions of clay, sand and silt, and bulk density, followed by ANN, when the database was not large enough. With the size of the database increasing, the ANN model becomes increasingly more efficient. Since ANN can use the mini-batch method to train the model without increasing computational costs, our results suggest that selecting a suitable method to calculate soil hydraulic parameters should consider computational cost and estimation accuracy when the size of the database increases.