The complexity inherent in medical diagnosis is compounded when confronted with patients experiencing polytrauma, necessitating a multifaceted approach to ensure accuracy, efficiency, and reliability. In this study, we address the challenge of predicting mortality risk in polytrauma patients through an experimental analysis of various machine learning algorithms. To this end, we have curated a dataset comprising clinical data of male patients afflicted with multiple traumas, treated at the Anesthesiology and Intensive Care Department for combined trauma cases within Ukrainian hospital. Employing a diverse range of optimized machine learning techniques, we assessed their efficacy in classification task using seven performance indicators. Our findings reveal that neural network models exhibit the best performance, particularly in terms of F1-score, Matthews Correlation Coefficient (MCC), and Cohen’s kappa score, surpassing traditional statistical methods. This underscores the potential of neural networks in accurately predicting mortality risk in polytrauma patients, thereby facilitating more informed clinical decision-making processes.