A catastrophic event, such as an earthquake, nuclear disaster, pandemic, or terrorist attack, can result in tens of thousands of casualties, destroy infrastructure, displace communities, paralyze the economy, and trigger cascading effects on critical systems and national security. In such cases, rapid and decisive action is essential for mass casualty mitigation and population protection. This work aims to advance applied scientific knowledge and training in national and public health emergency response and logistics by developing the RealOpt-Contingency computational platform. RealOpt-Contingency supports all-hazard disaster response by providing tools for logistics analysis, inventory management, and computational modeling. The platform enables users to more effectively (1) establish camps and medical facilities for affected populations; (2) design facility layouts for optimal use and safety; (3) optimize relief supply distribution; (4) determine the required rations, water, fuel, and supplies for each camp and medical facility; (5) calculate transportation labor, resource requirements, and optimize routes; (6) develop distribution plans from incident LSA to LSA hubs, camps, and medical facilities; (7) design decontamination and dispensing sites; (8) model epidemiological disease/contamination plumes; and (9) track the movement of displaced populations for real-time reconfiguration. The user-friendly graphical interface enables users to outline the affected area, design facility layouts, input inventory levels, population estimates, and demand requests. The backend system translates this data into appropriate mathematical formulations and simulation parameters. RealOpt-Contingency includes powerful computational-optimization engines, featuring resource allocation, transportation and routing algorithms, disease spread modeling, facility layout heuristics, inventory control processes, facility location optimization, agent-based simulations, and machine learning for influence network prediction. Its modular design allows for continuous technological advancements and adaptation based on real-world input. This system facilitates experimentation, operational analysis, and decision support for disaster preparedness, planning, and response. It enables decision-makers to understand tradeoffs, competing goals, and interdependencies during disaster response. We illustrate the application of RealOpt-Contingency in (a) COVID-19 pool testing optimization, and prospective disease prediction and validation; (b) Fukushima radiological emergency response for sheltering, screening, decontamination, and health registry management; (c) Haiti earthquake response, supply and inventory management, routing, and distribution; and (d) National USPS medical countermeasure dispensing model.