Spectral analysis is a useful tool for the rapid and accurate prediction of soil properties. Our objective is to select the best model for predicting the three soil cation concentrations ([Na⁺], [Mg²⁺], and [Ca²⁺]) and sodium adsorption ratio (SAR). Three methods were applied, i.e., stepwise multiple linear regression (SMLR), partial least-squares regression (PLSR), and support vector machine (SVM). Estimation models for four soil properties were developed using three different spectral processing and transformation techniques, i.e., reflectance (R_e), logarithm of reciprocal R_e (LR), and standard normal variable of R_e (SNV) were used. A total of 36 models were established. Of these, 27 models for [Na⁺], [Mg²⁺], and [Ca²⁺] were not applicable for subsequent prediction, because the coefficients of determination (R²) were not high (0.224–0.689), and their relative percent deviations (RPD) were all smaller than the 1.4 threshold. However, the models for SAR∼R using PLSR (R²=0.728 for calibration and 0.661 for validation, RPD=1.43), SAR∼LR using SVM (R²=0.791 for calibration and 0.712 for validation, RPD=1.81), and SAR∼SNV using SVM (R²=0.878 for calibration and 0.814 for validation, RPD=2.13) were valid for further prediction. Finally, SAR∼SNV using SVM was selected as the best model. There are intrinsic factors resulting in an unsatisfied model performance.