Sepsis is a complex, deadly, and difficult-to-diagnose disease characterized by anomalies in numerous life-threatening organ failures caused by an improper host response to an infecting organism such as bacteria, fungi, or viruses. Patient characteristics such as age and immunologic state, infection factors, and environmental factors such as nutritional status affect sepsis prognosis and make it difficult and a common cause of mortality. This project aimed to identifysepsis prognostic biomarkers by identifying significantly differentially expressed biomarkers across patient groups, then developing and evaluating a classification model that can help predict patients' prognosis. The project used input data consisting of 368 protein measurementsrepresented as Normalized Protein expression. These data have been preprocessed, split, and analyzed using the Wilcoxon rank-sum test to identify the significantly expressed biomarkers in each patient's subgroup, one in the ICU admission and six in the non-survived subgroups. These significantly expressed biomarkers were Volcano plotted, then integrated into different supervised and unsupervised multivariate statistical models. The best prognosis models for ICU admission were the KNN models based solely on either procalcitonin or C-reactive protein with AUCs of 1.00 (95% Cl: 1.00-1.00). The best prognosis model for the 28 days mortality was the KNN model of the tenascin-C with an AUC of 1.00 (95% Cl: 1.00-1.00). However, further studies are suggested using a larger sample size in order to lessen the likelihood of bias. Some of the identified significantly expressed biomarkers, procalcitonin, and CRP, could generate KNN models with high AUC that can be used to prognosis the ICU admission or the 28 days of mortality due to sepsis.