In the aftermath of natural disasters, first responders are responsible for conducting search and rescue operations, assessing the extent of damage, and initiating recovery efforts. These activities play a pivotal role in mitigating the impact of disasters and facilitating the restoration of normalcy. Yet, they face formidable challenges due to the necessity for rapid, time-sensitive decisions amidst an environment where information is both abundant and fragmented, and conditions evolve swiftly. This underscores the pressing need for more robust decision-making frameworks that enhance the effectiveness of responses in dynamic disaster environments. In response to these challenges, this study introduces the First Responders System (FiReS), an advanced system designed to optimize first responders’ response plans by utilizing Semantic web technologies and Artificial Intelligence (AI). Central to the architecture of the FiReS system are two core components: a comprehensive ontology and an intelligent agent. The ontology provides a structured framework that organizes disaster-related data, ensuring consistent interpretation and integration across diverse sources. Concurrently, the intelligent agent utilizes advanced machine learning algorithms to process this data and generate actionable recommendations. This strategic integration streamlines the decision-making processes and enhances the system's adaptability to evolving emergency scenarios. Overall, the proposed architecture positions the FiReS system as a significant advancement in the field of post-disaster response.