Frequent flood disasters pose a serious threat to people's lives and property safety. Rapid and accurate monitoring of flood inundation areas and their dynamic changes is crucial for disaster emergency response and assessment. To meet this demand, this study uses the flood disaster in Zhuozhou City, Hebei Province, from July to August 2023 as a study case. Utilizing Sentinel-2 multi-temporal optical imagery, we conduct the identification and dynamic change monitoring of floodwater bodies. By integrating spectral reflectance with various water body indices, we construct comprehensive identification features and separately developed Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) models for water body extraction. The results indicate that the RF model exhibits a water body identification accuracy exceeding 98.84% across different time phases, with a Kappa coefficient greater than 0.97. Furthermore, its stability and efficiency surpass those of the SVM and ANN models. Based on the time-series monitoring results obtained from RF, the complete process of "rapid inundation - slow recession" during the Zhuozhou flood disaster is revealed. During the recession phase, due to topographical and drainage conditions, some areas experience persistent waterlogging. This study combines the accessibility of Sentinel-2 data with the efficiency of the RF model, offering a high-precision and high-efficiency emergency monitoring solution for flood disasters.