Future of Work and Digital Management Journal 3092-720X In Press 2026 09 01 Designing an Intelligent Earthquake Crisis Management Framework for Megacities Using an Integrated C4ISR System: A Hybrid Approach Based on Mathematical Modeling and Artificial Intelligence Designing an Intelligent Earthquake Crisis Management Framework for Megacities Using an Integrated C4ISR System: A Hybrid Approach Based on Mathematical Modeling and Artificial Intelligence 1 18 EN Journal Article 2026 02 28 <p class="Abstract"><span lang="EN-GB" style="font-size: 9.0pt; line-height: 115%; font-family: 'Museo Sans 300'; letter-spacing: -.2pt;">The increasing concentration of population and the growing complexity of megacities have transformed earthquake crisis management into a fundamental challenge. Despite advances in emerging technologies, there remains a gap in developing an integrated framework that combines the dimensions of command, control, communications, and information with intelligent decision-making. The present study aimed to design and validate an intelligent framework for earthquake crisis management in megacities based on an integrated C4ISR system and a hybrid approach combining mathematical modeling and artificial intelligence. Drawing on the paradigm of critical realism and the system dynamics approach, the proposed model simulates the complex interactions among technical, informational, communicational, and organizational components and simultaneously optimizes four key objectives: minimizing human casualties, reducing response time, maximizing resource allocation efficiency, and enhancing situational awareness. To solve the multi-objective optimization problem, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was integrated with a deep neural network model (LSTM-CNN) and self-organizing maps (SOM) to enable prediction, continuous learning, and the identification of hidden crisis patterns. The simulation of three earthquake scenarios in Tehran showed that the proposed framework effectively manages conflicts among objectives and provides optimal strategies for different crisis conditions. Sensitivity analysis indicated that interorganizational coordination and network bandwidth had the greatest impact on system performance and were more influential than many hardware components. Moreover, the system’s dynamic learning capability increased its accuracy and response speed across operational cycles. By presenting a framework based on C4ISR, artificial intelligence, and mathematical modeling, this study represents an effective step toward data-driven crisis management and the enhancement of megacity resilience against earthquakes.</span></p> https://journalfwdmj.com/index.php/fwdmj/article/download/292/291