Introduction Intracranial hypertension (IH) is a harbinger of secondary brain injury in patients suffering from traumatic brain injury (TBI), can be mitigated at the Intensive Care Unit (ICU) and is associated with a poor prognosis. Current clinical practice consists of treating IH once it has occurred, by medical or surgical interventions. This is later than desired, as secondary injury has already been initiated. A pre-emptive approach may be preferable, and seems possible since many physiological variables that may aggravate IH are known and can be managed clinically. The aim of this research is to develop a machine learning method that is able to predict whether or not a patient will develop IH in the near future during ICU stay. Methods A cohort of 114 patients with TBI admitted to the ICU of Erasmus MC was selected. Long Short Term Memory (LSTM) models were trained and evaluated with 26 clinical variables to predict IH. The effect of the length of the minimal IH period, the length of the prediction window and the number of included variables was evaluated. Primary outcome measures were the model loss, accuracy, and Area Under the receiver operating characteristic Curve (AUC). Results We achieved a mean AUC of 0,83 [95% CI: 0,68-0,98] with a model predicting periods of ICP≥20mmHg lasting at least 15 minutes, using a prediction window of 30 minutes and using only the ICP and mean arterial blood pressure (MAP). All models showed decreasing training and validation loss values during the first few epochs of model training. Thereafter, the training loss continued to decrease while the validation loss started to increase. Conclusion We developed a LSTM model that was able to predict, with a mean AUC of 0.83 [95% CI: 0,68-0,98], the occurrence of IH after half an hour based on the ICP and MAP. Adding more clinical variables resulted in overtrained models.