Floods are one of the leading causes of damage, prompting mortality and substantial destruction to the structure and total economy of the affected nations. Remote sensing, satellite imagery, global positioning system, and geographic information system (GIS) are widely employed for flood identification to examine flood-related losses. Recently, accurate and automated flood detection models using remote sensing images have become effective for flood disaster management, risk manager, infrastructure planning, disaster rescue management, etc. Computer vision and deep learning (DL) models provide prompt and rapid flood detection in remote sensing images. In this aspect, this paper presents a multiverse optimization with a deep transfer learning-enabled flood detection (MVODTL-FD) technique for disaster risk management. In the proposed MVODTL-FD technique, remote sensing images are investigated for the effectual detection of floods. To accomplish this, the presented MVODTL-FD technique applies a guided normal filter (GNF) based image preprocessing approach to eliminate the noise. In addition, the proposed MVODTL-FD technique uses a deep convolutional neural network-based Squeeze Net model for feature extraction, and the hyperparameter process is performed using the MVO algorithm. At last, the flood detection process is performed using support vector machine (SVM) classification. For establishing the improved version of the MVODTL-FD method, a wide-ranging experimental analysis is performed. The MVODTL-FD model is rated higher in the comparative analysis than other DL models.