By leveraging drone remote sensing technology, this study addresses the limitations of traditional monitoring methods in expansive water bodies, such as restricted sampling points, data acquisition challenges, and insufficient spatiotemporal resolution. It overcomes challenges, including synchronous data acquisition in dynamic tidal bore environments and nonlinear relationship modeling. Focusing on Qibao and Yanguan stations along the Qiantang River, the research integrates in-situ water quality data and multispectral remote sensing images during tidal bores. Machine learning models—Support Vector Machine Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—were developed to invert suspended sediment concentration (SSC) and turbidity. Bayesian Optimization was applied to enhance model performance. Results demonstrate that the XGBoost model optimized by the Bayesian algorithm achieved superior accuracy, with determination coefficients (R²) of 0.89 (SSC) and 0.93 (turbidity), and reduced root mean square errors to 310.54 mg/L and 33.36 NTU, confirming model stability and predictive capability. Inversion results revealed abrupt SSC and turbidity surges near bridge piers during flood tides (peaking at 6000 mg/L and 820 NTU), indicating intense bed scouring by tidal bore dynamics. The model effectively captures spatiotemporal patterns of water quality parameters and provides an efficient solution for dynamic tidal bore monitoring, highlighting the potential of integrating multispectral imagery with machine learning. This approach offers a novel framework for highfrequency water quality assessment in turbulent hydrodynamic environments.