Urban flooding significantly impacts populations and often coincides with heavy rainfall, making optical satellite observation challenging due to cloud cover. This study proposes a novel approach using synthetic aperture radar (SAR) sensors, which can penetrate clouds, to classify flooded urban areas. The framework employs a 3-D convolutional neural network (3-D CNN) to process multitemporal SAR data from Sentinel-1 (S-1). The dataset included 24 S-1 scenes with Dual VV and VH polarization from March 2019 to February 2020, divided into two co-event images, 18 preevent images, and four postevent images. The 3-D CNN achieved an average overall accuracy of 70.3% and a peak accuracy of 71.8%. These results demonstrate the 3-D CNN's potential to accurately estimate flood extent and identify flood-prone areas, supporting early detection and flood prevention in other cities. © 2008-2012 IEEE.