Background and Objectives: Trauma remains a leading global cause of preventable mortality, and outcome prediction tools are essential for both triage and resource allocation. Timely and effective medical response to trauma patients is of the essence. While there are already some widely accepted trauma scores, our aim was to build a more precise model tailored to our cohort as a framework for future research. Materials and Methods: A retrospective cohort of 91 patients was analyzed, using several clinical and paraclinical factors to build a logistic regression model that predicted 30-day mortality. Results: After adjusting for collinearity, our final multiple regression model, comprising systolic blood pressure, glycemia, urea serum levels and number of fractures, showed an excellent model fit (McFadden R² = 0.682; AUC = 0.94) for predicting 30-day mortality. Systolic blood pressure was significantly associated with mortality (OR = 0.944, 95% CI: 0.920–0.969). In our cohort it seems to surpass the performance of The Trauma and Injury Severity Score (McFadden R² = 0.56; AUC = 0.90). Conclusions: Commonly available clinical parameters could contribute to risk stratification, highlighting the prognostic importance of hemodynamic instability and metabolic response. This study was conducted on a small cohort as an exploratory analysis. Further research and validation based on multicentric cohorts are needed.