Soil moisture data is essential for estimating groundwater storage anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) data, but the general lack of direct in-situ root-zone soil moisture observations has typically resulted in a reliance on modelled soil moisture estimates instead. These model-simulated soil moisture profiles – upper (0 to 10 cm), lower (10 to 100 cm), and deep layers (100 to 200 cm), are characterized by large uncertainties due to the simplification and parameterization of soil moisture processes in hydrological models. It is thus crucial to account for these uncertainties and understand how they affect the estimation of groundwater storage changes based on GRACE data. In this study, we evaluated the contributions and impacts of different soil moisture profiles on GRACE-derived groundwater storage (between 2002 and 2016) modelling uncertainties over the Murray Darling Basin (MDB) using statistical and machine learning regression. We observed that the lower layer exhibited the strongest correlation with base GWSA, particularly during 2006 to 2009 (r = 0.99, RMSE = 7.50 mm). Bootstrap analysis indicated that the lower layer consistently had the largest absolute coefficient weights, signifying its predominant influence on GWSA predictions. The deep layer contributed the least during 2010 to 2013, while the upper layer was highly dynamic and introduced a 26.8 % more uncertainty rating when compared to the lower layer. Regression analysis showed the lower layer maintained the smallest confidence interval widths, confirming its reliability. The Monte Carlo resampling corroborated these findings, with the lower layer maintaining the most consistent relationship with base GWSA across all periods. The lower layer’s steadier state and lower susceptibility to surface disturbances provided more accurate predictions than other layers. This study advances the modelling of groundwater storage from space by improving our understanding of the uncertainties introduced by the different soil moisture layers. It will be helpful for better and accurate freshwater reporting and management.