Global soil salinization and sodification threaten food security by causing excessive salt accumulation in soils, degrading productivity. However, their spatiotemporal patterns have not been well documented due to the lack of high-accuracy estimates of soil salinity and sodicity with a long-term perspective. Here we propose a novel framework combining the Box-Cox transformation that addresses the skewed distribution of samples and more critical predictors as well as remote sensing indices, to predict global soil salinity (electrical conductivity of the saturated soil extract, ECe) and sodicity (exchangeable sodium percentage, ESP) from 1980 to 2024 with a 1 km x 1 km resolution using random forest. Model accuracy is higher than previous studies, with root mean square error (RMSE) of ECe and ESP as 2.24 and 6.04 respectively, and an R2 of 0.65, and 0.60 respectively. The implementation of the Box-Cox transformation significantly improved the model performance (R2) by approximately 100 % (from 0.35 to 0.79 for ECe and 0.59 to 0.85 for ESP), while the additional predictors further enhanced the performance (R2 increased by 15 %), ranking in the top 30 % of the feature importance list. Results revealed that global multiple-year average salinization and sodification are primarily concentrated in arid regions characterized by low precipitation and high temperatures. We also found a significant increasing trend of soil salinization in 20 % of global land and of sodification in 48 % of global land from 1980 to 2024, both most pronounced near the equator, as well as in central and eastern North America, Europe, southeastern China, and Mongolia. This study provides updated long-term soil salinity and sodicity maps with improved accuracies, offering critical insights for sustainable land management under climate change, serving as an essential resource for addressing food security and land degradation challenges worldwide.