Conventional disaster response paradigms are fundamentally constrained by reliance on human-centric intelligence, which introduces significant cognitive and heuristic biases, resulting in sub-optimal decision-making and inefficient resource allocation under high-stress, dynamically evolving scenarios. This research posits a novel framework that transcends these limitations by operationalizing a cyber-physical System of Systems (SoS) architecture where a heterogeneous fleet of Uncrewed Systems (UxS) functions as a high-fidelity Digital Twin (DT). The cognitive core of this framework is a Model-Based Artificial Intelligence (MBAI) engine, a synergistic integration of Model-Based Systems Engineering (MBSE) methodologies with advanced AI. This MBAI leverages pre-compiled Pattern Libraries (PL), constraint-based mathematical models, and predictive physics-based and stochastic simulations (ModSim) to explore potential state-space evolutions and derive optimal control policies. The DT provides a real-time, synchronous emulation of the physical environment by assimilating multi-modal data streams from UxS sensor suites and disparate inter-agency systems. The system provides the functional mechanisms to enforce data consistency across a federated architecture, thereby adhering to the single source of truth principle, and meticulously documents data lineage to maintain a forensically sound chain of custody for all collected data objects. Architecturally, the complex UxS fabric is conceptualized using an SoS methodology and decomposed via the rigorous Systems Engineering (SE) Vee model to ensure robust integration, verification, and validation of all subsystems. Operationally, the MBAI-driven DT autonomously establishes comprehensive situational awareness, performs multi-objective optimization to orchestrate a minimum viable plan, and dynamically allocates assets to mitigate impacts in high-vulnerability sectors. The system is designed to interface directly with critical infrastructure nodes such as power, communication, and transportation to model interdependencies, predict and preempt cascading failures, and reinforce Emergency Support Functions (ESF). Initial field deployment of physical UxS assets focuses on high-resolution geospatial data acquisition to calibrate and validate the DT's underlying models. The longitudinal, high-fidelity data generated throughout the response lifecycle (from heroic to disillusionment phases) is invaluable for post-hoc forensic analysis, model refinement, and provides a quantitative, auditable basis for Federal Emergency Management Agency (FEMA) assessments and insurance claims adjudication, ensuring verifiable data provenance and maintaining a strict chain of custody for evidentiary purposes. This extensible framework is agnostic to disaster typology and is engineered to enhance operational resiliency across a spectrum of catastrophic events, from hydrometeorological and geophysical events to technological and anthropogenic crises.