Effective triage during mass casualty incidents is critical, requiring emergency nurses to make rapid decisions in high-stress, resource-limited environments. Although structured systems such as simple triage and rapid treatment and JumpSTART remain foundational, they structure but are vulnerable to human error under cognitive overload. As disasters grow more frequent and complex owing to climate change, pandemics, and conflicts, there is a pressing need for innovative tools that can help frontline responders manage these challenges effectively. Artificial intelligence-powered triage and decision-support systems are emerging as promising solutions in disaster response. By leveraging machine learning and real-time data, these systems enhance triage accuracy, optimize resource allocation, and improve situational awareness. Real-world applications, including artificial intelligence-assisted tele-triage in rural settings and postearthquake injury prediction in Japan, illustrate their expanding utility. However, artificial intelligence integration also presents challenges. Challenges in implementing these technologies involve data set bias, limited transparency, and excessive reliance on automation, all of which can erode clinical judgment and trust. Without clear protocols and adequate training, these tools risk hindering rather than enhancing care. Active involvement of emergency nurses in system codesign and the establishment of override mechanisms are essential to safeguard clinical integrity. Building artificial intelligence literacy, integrating simulation-based training, and promoting ethical implementation are critical next steps. When thoughtfully applied, artificial intelligence can augment emergency nursing practice, enabling more accurate, timely, and coordinated care in disaster response.