This study presents a method of predicting the soil water retention curve (SWRC) of a soil using a set of measured SWRC data from a soil with the same texture but different initial void ratio. The relationships of the volumetric water contents and the matric suctions between two samples with different initial void ratios are established. An adjustment parameter (β) is introduced to express the relationships between the matric suctions of two soil samples. The parameter β is a function of the initial void ratio, matric suction or volumetric water content. The function can take different forms, resulting in different predictive models. The optimal predictive models of β are determined for coarse-grained and fine-grained soils using the Bayesian method. The optimal models of β are validated by comparing the estimated matric suction and measured data. The comparisons show that the proposed method produces more accurate SWRCs than do other models for both coarse-grained and fine-grained soils. Furthermore, the influence of the model parameters of β on the predicted matric suction and SWRC is evaluated using Latin Hypercube sampling. An uncertainty analysis shows that the reliability of the predicted SWRC decreases with decreasing water content in fine-grained soils, and the initial void ratio has no apparent influence on the reliability of the predicted SWRCs in coarse-grained and fine-grained soils.