Detecting earthquake-induced bathymetric changes helps to understand the geomorphologic process of tufa lakes. Traditional field measurement methods are difficult for spatially complete and continuous bathymetric mapping. Multi-temporal high-resolution optical satellite images are cost-efficient data used for bathymetric change detection. However, for detecting bathymetric changes in tufa lakes, collecting high-density depth calibration data and constructing highly robust water depth inversion models pose certain challenges. This study takes Huohua Lake before and after the Jiuzhaigou Earthquake as the research object, and carries out the bathymetric change detection based on high-resolution remote sensing data. Initially, the WorldView-2 (WV-2) multispectral images obtained before and after the earthquake under the water-storage state of the lake were used as the data source, and the unmanned aerial vehicle (UAV)-based measurement under the water-free state of the lake after the earthquake was used as the bathymetric calibration and validation data. Then using satellite-derived image reflectance, we constructed two-phase bathymetric models with machine learning methods, namely random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP). The comparison results with classical regression models indicate that machine learning-based water depth inversion models are generally superior. Specifically, the R² (coefficient of determination) of the optimal model RF reach 0.85 and 0.91, with RMSE (root mean square error) of 1.40 m and 1.08 m. The bathymetric difference maps generated from water depth inversion results reveal that during the period from October 2016 to January 2022, the core area of Huohua Lake experienced more erosion than accretion due to the earthquake-induced flooding. The spatial patterns of changes show that the erosion mainly located in the raised tufa mound area, while the accretion was concentrated in the shallow flat area. This study provides a remote sensing approach for quantifying bathymetric changes in tufa lakes after extreme geological disasters.