Soybean ( Glycine max) growth is severely constrained by the high salt content of saline-alkali soils, leading to substantial declines in yield and quality. Enhancing soybean tolerance to saline-alkali stress has significant economic and ecological implications. However, current investigations into the regulatory mechanisms underlying soybean responses to such stress, particularly those integrating physiological traits with transcriptomic analyses, remain inadequate. In this study, seven physiological indicators exhibited significant variation among soybean cultivars grown under saline-alkali versus normal conditions, with notable correlations observed among their rates of change. The salt tolerance rankings derived through principal component analysis (PCA) combined with the membership function value method were robustly validated using the technique for order preference by similarity to an ideal solution (TOPSIS), establishing a reliable evaluation and verification framework. Through the analysis of differentially expressed genes in the transcriptome, a total of 4,582 genes were found to be differentially expressed, among which 39 genes were differentially expressed in all tissues and varieties. Enrichment analysis revealed that different expressed genes were predominantly involved in stress response and metabolic regulation pathways. Weighted gene co-expression network analysis (WGCNA) further identified the gene modules closely associated with each physiological trait. By integrating the DEGs with module hub genes, 13 core candidate genes were identified. Functional annotation and promoter analysis of these genes preliminarily revealed potential regulatory pathways conferring salt tolerance. Collectively, these findings provide a comprehensive basis for elucidating the molecular mechanisms of soybean adaptation to saline-alkali conditions.