Soil structure is one of the key soil water-physical properties that determine the water-air regime and ultimately affect soil fertility. This study aimed to test different machine learning (ML) methods in combination with environmental variables (soil and climate) and remote sensing data derived from Landsat 8 for prediction of key structure parameters of topsoil (0-25 cm) in semi-arid areas (Trans-Ural steppe zone, Republic of Bashkortostan, Russia). The all studied soil types (Chernozems (n = 24), Solonchaks (n = 9)) and Solonetzes (n = 12)) characterized by "excellent" aggregate state (the average structural coefficient (Ks) was 6.52, 11.23 and 5.70) and "good" resistance of aggregates to destruction by water (soil aggregate stability coefficient (Ksas)-0.67, 0.65 and 0.70, respectively). The soils had a high proportion of agronomically valuable aggregates (0.25-10 mm, mesoaggregates (MEA)), and a low proportion of blocky/lumpy (>10 mm, macroaggregates (MAA)) and fine/dusty (<0.25 mm, microaggregates (MIA)) ones. In particular, the average share of MIA, MEA, and MAA in Chernozem was 7.63, 83.20, and 11.73%, and in Solonchak, 4.24, 87.91, and 9.74%, respectively. After wet sifting, the water-resistant macroaggregates (WSMAA) were not identified (they were destroyed by water) in all studied soils; the proportion of water-stable mesoaggregates (WSMEA) in Chernozems was 65.92 and microaggregates (WSMIA)-39.67; Solonchaks-74.95 and 22.54; Solonetz soil-66.77 and 33.22%; respectively. Under the ML framework, the best model was achieved for Ksas predictions (R2 = 0.50 and RMSE 0.17), where spectral indices (NDWI, EVI, SAVI, and NDVI) were the main predictors. Other ML techniques explained 22-30% variance of the remaining properties. The findings of this study can be valuable in further endeavors for soil water-physical mapping and accelerate the adoption of measures for land management/reclamation planning for landscapes with similar (arid and semi-arid) natural climatic conditions.