With advances in digital health technologies and proliferation of big biomedical data in recent years, applications of Machine Learning (ML) in healthcare and medicine have gained significant attention. Modern Intensive Care Units (ICUs), in particular, are equipped to generate rich multimodal clinical data on critically-ill patients. In this thesis, we focus on applying machine learning techniques for prognostication of Traumatic Brain Injury (TBI) patients in ICU, which is the leading cause of death and disability among children and adults of age less than 44. We present two case studies to demonstrate the feasibility and applicability of machine learning techniques: one for mortality prediction in TBI patients and the second for extracting patterns from physiological data collected from TBI patients. For the case study I, clinical data including demographics, vital signs, and physiological data for the first 72 hours of TBI patients were extracted from the Medical Information Mart for Intensive Care III (MIMIC III) database. Several traditional supervised machine learning algorithms such as artificial neural network, support vector machine, and logistic regression were employed to construct prediction models. Bagging and Voting techniques were implemented to improve the performance of these algorithms. By comparing the performances of these algorithms, we showed that deploying voting techniques on several different ML models can improve the overall performance. These algorithms obtained the highest Area Under receiver operating characteristic Curve (AUC) of 0.91. For the case study II, an exploratory, secondary analysis of physiologic data of TBI patients from the Phase III trial of Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment (PROTECT) was performed. Subspace clustering was used to extract relationships between various physiologic variables. For both studies, 10-fold cross validation was used for evaluation purposes.