This study aims to develop models for predicting rare events, specifically elevated intracranial pressure (ICP) in patients with traumatic brain injury (TBI). Using time-series data of ICP, we created and evaluated several machine learning models, including K-Nearest Neighbors, Random Forest, and logistic regression, in order to predict ICP levels exceeding 20 mmHg – acritical threshold for medical intervention. The time-series data was segmented and transformed into a tabular format, with feature engineering applied to extract meaningful statistical characteristics. We framed the problem as a binary classification task, focusing on whether ICP levels exceeded the 20 mmHg threshold. We focused on evaluating the optimal model by comparing the predictive performance of the algorithms. All models demonstrated good performance for predictions up to 30 minutes in advance, after which a significant decline in performance was observed. Within this timeframe, the models achieved Matthews Correlation Coefficient (MCC) scores ranging between 0.876 and 0.980, where the Random Forest models showed the highest performance. In contrast, logistic regression displayed a notable deviation at the 40-minute mark, recording an MCC score of 0.752. The results presented highlight potential to provide reliable, real-time predictions of dangerous ICP levels up to 30 minutes in advance, which is crucial for timely and effective medical interventions.