Using the Landsat-8 OLI multispectral satellite remote sensing images covering typical islands and collected water depth data, this study comprehensively invert the water depth of the target sea area using the traditional multiple linear regression model, back propagation neural network model and random forest model in machine learning. The inversion accuracy of the three methods is evaluated. The results show that compared with the multiple linear regression model, machine learning methods have higher water depth inversion accuracy. The water depth inversion accuracy of the random forest model is the highest with a mean absolute error of 1.94 m and a mean absolute percentage error of 18.29%, and the robustness of the model is better, and the overall accuracy is significantly improved compared with that of the multiple linear regression model. This study compares the performance of shallow water bathymetric models built using the three methods, providing reference value for subsequent research on obtaining high-precision shallow water bathymetric information more efficiently.