【Objective】Soil salinity is an important indicator for evaluation of soil salinization in arid regions. It is of great practical significance to grasp real-time information about salinization to disaster prevention, ecology stabilization and harmonization of human-land relationship in this area. 【Method】A total of 400 soil samples were collected from the Weigan River-Kucha River Delta Oasis in the Xinjiang Uygur Autonomous Region of China in October 2017, and prepared, separately, with distlled water into suspensions, 5:1 in ratio, from which soil extracts were obtained for analysis of electrical conductivity (EC) and VIS-NIR (visible-near infrared) spectral reflectances in the laboratory. Based on the obtained data and the Monte Carlo cross validation (MCCV), 364 samples were determined to be valid. After the raw spectrum reflectances (R) were pre-processed with differential, absorbance (Abs), continuum removal (CR) and three others, 21 spectal indices were selected and established. Then based on the 21 optimal spectral indices, EC was assessed using the back propagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM), compared to the partial least squares regression (PLSR), and validated with its root mean square error (RMSE), determination coefficient (R²) and ratio of performance to interquartile range (RPIQ). 【Result】Results show: the 21 optimal spectral indices based on R and its pretreated data are R_NDSI(R₂₀₀₆, R₂₂₅₇), R_DSI(R₁₈₈₂, R₂₀₁₀), R_RSI(R₂₀₀₆, R₂₂₅₇), R_FD_NDSI(R₁₃₇₆, R₂₁₄₂), R_FD_DSI(R₁₃₇₆, R₂₁₄₂), R_FD_RSI(R₁₉₁₃, R₂₁₄₂), R_SD_NDSI(R₄₁₆, R₂₄₇₀), R_SD_DSI(R₈₉₄, R₁₃₇₃), R_SD_RSI(R₆₈₉, R₂₃₅₅), ABS_NDSI(R₂₀₀₅, R₂₁₆₈), ABS_DSI(R₂₀₀₆, R₂₂₅₇), ABS_RSI(R₂₀₀₆, R₂₁₆₈), ABS_FD_NDSI(R₈₇₆, R₂₄₉₀), ABS_FD_DSI(R₁₃₇₆, R₂₁₂₃), ABS_FD_RSI(R₁₉₁₃, R₂₁₄₂), ABS_SD_NDSI(R₁₀₈₁, R₁₇₂₅), ABS_SD_DSI(R₈₅₈, R₁₃₇₄), ABS_SD_RSI(R₇₀₉, R₂₃₅₅), CR_NDSI(R₂₁₁₉, R₂₂₆₁), CR_DSI(R₂₁₁₉, R₂₂₆₁), and CR_RSI(R₂₁₁₉, R₂₂₆₁), among which R_FD-RSI (R₁₉₁₃, R₂₁₄₂) is the optimal (r= 0.649) one. Compared with the PLSR, the machine learning algorithm (MLA) could significantly improve accuracy of the model, with the R² increased by 34.55%. Among the three models using the machine learning algorithm, ELM was the best (R² = 0.884, RMSE = 3.071 mS?cm^-1, RPIQ = 2.535). 【Conclusion】In this study, different spectral pretreatment methods were used to obtain 21 optimal spectral indices. In constructing the spectral indices in this study, besides considering the remote sensing mechanism, it is advisable to explore in depth more implicit information. Compared with the traditional linear model, the MLA-based soil EC estimation model is obviously higher in accuracy. All the findings in this study may serve as a scientific reference for quantitative estimation of soil salinity in arid regions.