Abstract This research explores the intersection of the Internet of Things (IoT) and Intelligent Spatial Multi-Objective Optimization (ISMOO) in disaster management. The integration of IoT and ISMOO heeds immense promise for improving the efficiency, effectiveness, and responsiveness of disaster management strategies. This research identifies notable gaps in the existing literature, including insufficient empirical evidence, the absence of comprehensive models, and a lack of in-depth discussions on data privacy and scalability. The research aims to fill these gaps through empirical studies, comprehensive modeling, and proposals for scalable and robust systems. By providing comprehensive, empirical, and interdisciplinary insights, this research contributes significantly to the field and seeks to optimize the synergy of IoT and ISMOO within the context of disaster management, promising transformative improvements for the sector. The present research proposes a model based on the Criteria Importance through Inter-Criteria Correlation (CRITIC) method for selecting suitable sites using type-2 neutrosophic numbers (T2NN) and the Geographic Information System (GIS). A selected group of experts determines the effective objectives and attributes of the study and assigns linguistic variables to evaluate the relative importance of each objective and attribute. The T2NN-CRITIC method is then used to evaluate the weights and rank the objectives and attributes. By integrating the spatial attribute layers and global weights using ArcGIS Pro software, the proposed model selects the most suitable site for temporary shelter in the flood-prone region of Dahab, Egypt. The sensitivity analysis confirms the stability of the study model. This research also proposes a practical framework for enhancing disaster management strategies for flooding. The model utilizes a two-phase approach that combines multi-criteria decision making with forecasting models to select optimal drone takeoff and landing locations for improved disaster response. The neutrosophic ordinal priority approach is used to weight the criteria and sub-criteria, and the model is tested using a case study from the Egyptian Mediterranean Coast. Findings reveal that Port Said governorate is the most vulnerable, and the top 10 suitable sites for drone takeoff and landing are suggested for this region. The model was confirmed to be stable through sensitivity analysis.