The aim of the study was to estimate the properties of the salt-affected soils (SAS) using hyperspectral remote sensing. The study was carried out on typical SAS from 372 locations covering 17 coastal districts from west coast region of India. The spectral reflectance of processed soil samples was recorded in the wavelength range of 350-2500 nm. The full data set (n=372) was split into two as calibration dataset (n=260, 70%) to develop the model and validation dataset (n=112, 30%) to evaluate the performance of the model independently. The spectral data were calibrated using the laboratory estimated soil properties with five different multivariate techniques: (a) linear – partial component regression (PCR) and partial least square regression (PLSR) and (b) non-linear– multivariate adaptive regression spline (MARS), random forest (RF) and support vector regression (SVR). In general, the spectral reflectance from the soils decreased with increasing levels of salinity (electrical conductivity, EC). The wavelengths, 494, 673, 800, 1415, 1748, 1915, 2207 and 2385 nm showed peculiar absorption characteristics. The study showed significant achievement in predicting soil properties like soil pH, salinity (EC), bulk density (BD), soil available nitrogen (N), exchangeable magnesium (Mg), soil available zinc (Zn) and boron (B) with acceptable to excellent predictions (ratio of performance to deviation (RPD) ranged 1.48-2.06). Amongst predicted models, SVR, PLSR and PCR were found to be more robust than MARS and RF. The results of the study indicated that the visible near-infrared spectroscopy has the potential predict properties of the SAS.