Increasing salt-affected soils in any part of the landscape causes adverse impacts on productivity. It is the primary indicator of degradation processes in land and soils, especially in arid and semiarid regions worldwide. The spatial extent of salt-affected soils is a major environmental problem for flora and fauna ecosystems in any part of the area. It occurs due to the accumulation of salts within soil layers, causing negative impacts on soil fertility and physicochemical properties. Spatiotemporal dynamics of salinity are influenced by the salinization/alkalinization process, where the evaporation rate exceeds precipitation due to natural and anthropogenic activities. The present study aims to assess the salt-affected soils and their pH and electrical conductivity (EC) using Sentinel 2A-Multispectral Imager (MSI) image by analyzing multiple salinity indices. The analysis deployed multiple salinity indices to different MSI bands based on their spectral reflectance properties. Out of salinity indices, salinity index-2 (SI-2) and salinity index-5 (SI-5) were used to generate the prediction model for pH and brightness index and vegetation soil salinity index for soil electrical conductivity (EC). The results reveal that the NDSI is estimated at −0.211 to 0.115, and salinity index-1(SI-1) and SI-2 were estimated at 0.548–1.314 and −0.292 to 0.136, respectively. Wherein the EC in the whole area is noted as nonsaline (<2 dS m−1), low (2–4 dS m−1), and medium (4–8 dS m−1), and the pH values are classified as neutral (pH 6.5–7.3), slightly alkaline (pH 7.3–7.8), moderately alkaline (pH 7.8–8.4), and strongly alkaline (pH 8.4–9.0) for an area of about 49, 1290, 2366, and 349 ha, respectively. Out of all variables assessed, salinity index-2 (SI-2) and salinity index-5 (SI-5) were used to generate the best model for predicting pH as it has yielded high pH to standardized coefficient values. The multilinear statistical correlation was developed using in situ measurements, and salinity indices yielded values that indicate the R2 value of 0.24 and Root Mean Square Error (RMSE) at 1.61 for soil EC and the soil pH with the R2 value of 0.31 and RMSE at 0.635. It is noted that the poor prediction of values is due to the similar reflectance between the coarse loamy soils and salt-affected soils in the study area. The outcome of this study can be used to develop soil conservation planning activities on a watershed scale.