Abstract: Artificial intelligence has reshaped the landscape of knowledge services in emergency response, while also introducing new challenges. This paper proposes a comprehensive framework for AI-empowered emergency knowledge services, encompassing a cognitive framework, system architecture, and operational model. Starting from the cognitive dimension, an AI-integrated cognitive framework spanning physical, information, and social spaces is constructed, enabling the dynamic mapping of disaster scenarios through cross-modal learning. To address the integration of multi-source heterogeneous data, a three-layer knowledge organization model comprising attributes, services, and events is proposed, forming a structured emergency knowledge service system. Building upon this cognitive foundation, the study further designs a digital-intelligence driven emergency knowledge computing framework, which incorporates multimodal fusion, neuro-symbolic hybrid reasoning, continuous learning, and closed-loop feedback control, thereby establishing a complete data–information–knowledge–wisdom transformation chain. Based on the proposed system architecture, feasible operational models are further explored. By introducing a multi-agent collaborative mechanism, decentralized flows of emergency knowledge services are realized. Through a collaborative network composed of decision-makers, responders, and the public, a complete operational process is achieved, including demand articulation, topic identification, feedback generation, iterative updating, and response termination. This study not only enhances the level of intelligence in emergency knowledge services but also provides theoretical foundations and practical pathways for rapid and precise emergency response, promoting a paradigm shift in emergency management from experience–based response to knowledge–driven response.