Background <p>Traumatic patients usually suffer from several complex conditions that hinder their risk characterization. The aim of this study was to derive phenotypes of prehospital acute life-threatening trauma via nonsupervised artificial intelligence (AI) clustering methods.</p> Methods <p>This was a prospective multicenter study in adult trauma patients treated in prehospital care and transferred to the emergency department. The study included 147 ambulances, 4 helicopters, and 11 hospitals in Spain between 1 January 2021 and 31 August 2024. Epidemiological variables, trauma-related data, baseline vital signs and blood tests were collected. The primary outcome was all-cause 2-day in-hospital mortality.</p> Results <p>A total of 1474 patients were included, with a 2-day in-hospital mortality rate of 8.3%. The selected clustering method identified three clusters: the T-1 phenotype comprised 6.9% (101 cases) with a mortality rate of 93.1%, the T-2 phenotype represented 23.6% (348 cases) with a mortality rate of 68.1%, and T-3 represented 69.5% (1,025 cases) with a mortality rate of 10.6%. The T-1 phenotype mainly involves traumatic brain injuries, followed by thoracic trauma and burns; the T-2 phenotype presents a similar distribution; and the T-3 phenotype predominantly involves orthopedic trauma.</p> Conclusion <p>The AI method identified three clusters with implications for therapy and outcomes. This novel approach could help emergency medical services characterize trauma patients by providing benefits, treatment and resource optimization.</p>