To estimate the maximum dynamic shear modulus of undisturbed marine soil on the east coast of China, a reliable machine learning (ML) prediction model was established. First, a database of maximum dynamic shear modulus for undisturbed marine soils along the eastern coast of China was constructed. Then, four different ML models were applied, and their prediction accuracy was compared to determine the best model. Finally, in order to further improve the interpretability of the predictions, the SHAP(shapley additive explanations) method was used to evaluate the impact of each input parameter on maximum dynamic shear modulus. The results show that the gradient boosting (GB) model outperforms the other ML models in terms of prediction accuracy and generalization ability for new data. The depth has the most significant effect on the dynamic shear modulus of marine soil, followed by the plasticity index.