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Salt-affected soils are related to salinity (high content of soluble salts) and/or sodicity (excess of sodium), which are major leading causes of agricultural land degradation. This study aimed to evaluate the performances of three machine learning (ML) algorithms in predicting the soil exchangeable sodium percentage (ESP), electrical conductivity (EC_e), and salt-affected soil classes, from soluble salt ions. The assessed ML models were Partial Least-Squares (PLS), Support Vector Machines (SVM), and Random Forests (RF). Soil samples were collected from the High Valley of Cochabamba (Bolivia). The explanatory variables were the major soluble ions (Na⁺, K⁺, Ca²⁺, Mg²⁺, HCO₃⁻, Cl⁻, CO₃²⁻, SO₄²⁻). The variables to be explained comprised soil EC_e and ESP, and a categorical variable classified through the US Salinity Lab criteria. According to the model validation, the SVM and RF regressions performed the best for estimating the soil EC_e, as well as the RF model for the soil ESP. The RF algorithm was superior for predicting the salt-affected soil categories. Soluble Na⁺ was the most relevant variable for all the predictions, followed by Ca²⁺, Mg²⁺, Cl⁻, and HCO₃⁻. The RF and SVM models can be used to predict soil EC_e and ESP, as well as the salt-affected soil classes, from soluble ions. Additional explanatory features and soil samples might improve the ML models’ performance. The obtained models may contribute to the monitoring and management of salt-affected soils in the study area.