The distribution coefficient (Kd) plays a crucial role in predicting the migration behavior of radionuclides in the soil environment. However, Kd depends on the complexities of geological and environmental factors, and existing models often do not reflect the unique soil properties. We propose a multimodal technique to predict Kd values for radionuclide adsorption in soils surrounding nuclear facilities in South Korea. We integrated and trained three sub-networks reflecting different data domains: soil adsorption factors for physicochemical conditions, X-ray fluorescence (XRF) data, and X-ray diffraction (XRD) spectra for inherent soil properties. Our multimodal model achieved high performance, with a coefficient of determination (R2) of 0.84 and root mean squared error (RMSE) of 0.89 for natural log-transformed Kd. This is the first study to develop a multimodal model that simultaneously incorporates inherent soil properties and adsorption factors to predict Kd. We investigated influential peaks in XRD spectra and also revealed that pH and calcium oxide (CaO) were significant variables in soil adsorption factors and XRF data, respectively. These results promote the use of a multimodal model to predict Kd values by integrating data from different domains, providing a cost-effective and novel approach to elucidate the mechanisms of radionuclide adsorption in soil. Environmental implicationRadionuclide contaminated soil, likely to pose adverse effects for site remediation, needs to be managed via modeling approaches. Previous studies are limited in that they merely consider parametric Kd models or single machine learning models. This study aimed to investigate the adsorption characteristics of soil samples under various environmental conditions and estimate Kd by presenting multimodal models combining trained sub-networks for diverse experimental data. The results based on the Kd represent a significant advance in the management of radioactive contaminants in soil, promising safer and more sustainable environmental management practices. Overall, this study paves a new approach to estimating Kd.