Traumatic brain injury (TBI) is a complex condition requiring continuous monitoring in the ICU. This study uses multimodal monitoring (MMM) and machine learning to predict physiological incidents – high intracranial pressure (ICP), out-of-range systolic arterial blood pressure (ABP), and out-of-range diastolic ABP – up to an hour before onset. Using data from 36 TRACK-TBI patients, we extracted 3,528 features from seven physiological modalities. We evaluated multiple classifiers, with random forest performing best, particularly for high ICP prediction. Future work includes expanding the cohort, assessing site-specific biases, and integrating additional modalities.