Most current infrastructures for disaster management rely on satellite-centred, centralized AI systems. These systems have meant demonstrated high-resolution monitoring systems for a long time. Still, these systems are not robust to the mimicking of data bottlenecks, fragile infrastructures, and solely working in linear ways under extreme stress (central risk). We presented a biomimetic resilience framework inspired by ecological systems (mycorrhizal fungal networks, swarm intelligence of ants, and distributed neural networks of octopuses) to create adaptive, decentralized, and selfhealing AI ecosystems for disaster prediction and recovery. Different from a conventional paradigm of satellites, simulating an extreme disaster implies we can have an adaptive multiagent network across land, air, sea, and the binary systems we can create around behavior. These bio-inspired systems demonstrate resilience by dynamically reallocating resources, self-healing through redundancy, and evolution through feedback loops to propose applicable case studies (wildfire containment, urban inundation/flooding, and post-earthquake recovery) of biomimetic AI to real world challenges. The paper also combines ethical design principles, based on ecological balance and cultural inclusion, to ensure that AI-centric systems foster human trust and do not perpetuate anthropocentric biases. In contrasting biodigital resilience with satellite-based disaster intelligence, we articulate a paradigm shift away from robust mechanical systems to "living" AI architectures, which favor adaptability, diversity, and situational awareness. This synthesis takes the first steps toward the design of new disaster management systems, enabling organizers - from policymakers and engineers to scholars and academics - to design disaster-resilient societies that evolve with, not against, nature.