One of the most destructive natural catastrophes is flood, which seriously effects houses, infrastructure, and agricultural land, resulting in large financial losses and negative social effects. Ground surveys and hand inspections are too time-consuming, labour-intensive, and human error-prone traditional methods for evaluating postflood damage. Recent developments in machine learning and computer vision have created new avenues for automated, precise, and effective flood damage assessment. This research employs an ensemble deep learning framework that makes use of eight cutting-edge CNN architectures, such as VGG16, ResNet-50, and MobileNetV2. The objective is to use deep ensemble learning techniques to categorize flood detection. Our models were trained, tested, and validated using the FloodNet and flood area segmentation datasets. With a training accuracy of 99.8% and a test accuracy of 95.4%, the ensemble model performs better during the testing phase than a number of separate benchmark models. The suggested approach seeks to effectively forecast floods and carry out early evaluations of impacted regions. The suggested computer vision-based system seeks to provide near-real-time data on flood effects to help government organizations, disaster response teams, and insurance firms make educated judgments. This method offers a scalable way to improve postflood recovery operations by accelerating and improving the accuracy of damage assessment.