Floods are among the most destructive natural hazards, and their management is often challenged by the complexity of predicting and monitoring inundation dynamics. In recent years, the increasing availability of satellite imagery has greatly enhanced the ability to analyze flood propagation and assess its spatial and temporal behavior. This study aims to investigate the spatiotemporal characteristics of the 2019 flood along the Dez River by integrating Sentinel-1 Synthetic Aperture Radar (SAR) data, Sentinel-2 multispectral imagery, and the cloud-based Google Earth Engine (GEE) platform. Within this framework, pre- and post-event Sentinel-1 SAR images with VV polarization (Vertical–Vertical) were processed using radar backscatter differencing and optimal threshold selection to identify inundated areas, while the natural river corridor was extracted from Sentinel-2 data using the Normalized Difference Water Index (NDWI) and the unsupervised K-Means classification algorithm. The results show that the flood under investigation reached its peak on 4 April 2019, when the discharge exceeded 3,200 m³/s. The inundated area expanded dramatically from approximately 51.8 km² at the onset of the event (1 April) to more than 422 km² two days after the peak (6 April), representing an increase of more than elevenfold compared to the natural river extent. A distinct temporal lag was observed between the maximum discharge released from the dam and the largest inundation footprint, indicating limited flow conveyance within the main river channel and the inherently delayed process of floodplain water spreading. Furthermore, the prolonged retention of water in low-lying agricultural and marginal lands—even after the discharge had declined—points to weak natural drainage and a high potential for waterlogging across the region. Overall, the findings demonstrate that the combined use of radar and optical satellite data within the GEE environment provides a rapid, reliable, and accurate framework for flood-extent mapping in data-scarce regions. This integrated approach holds significant potential for improving flood risk management, supporting post-flood damage assessment, and contributing to the development of more advanced flood-forecasting and hydrodynamic modeling tools in the future.