Rationale, aims and objectives: Intracerebral hemorrhage (ICH), the second most common cause of stroke, has a high fatality rate. The establishment of mortality prediction models based on ICH patients and disease characteristics is very useful for clinical decision-making and corresponding treatment methods. Therefore, we used five machine learning methods to establish models for predicting in-hospital mortality in ICH patients and compared models’ performance. Methods: Model development and performance comparisons were performed using the medical information mart for intensive care (MIMIC-III) database. We took the maximum and minimum values of each index of 1143 ICH patients in the first, second and third days after admission as the input variables of the model, and established five machine learning models including random forest (RF), Gradient Boosting Decision Tree (GBDT), decision tree, Naïve Bayes and KNN. The most important feature variables were selected by the RF model and Least Absolute Shrinkage and Selection Operator (LASSO) method. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score were used as the assessment criteria of the model prediction effect. Results: After 5-fold cross-validation, the AUROC of RF, GBDT, Naïve Bayes, Decision Tree and KNN models were 0.92, 0.93, 0.9, 0.89, 0.89, respectively. The performance of GBDT was better than other prediction models. The accuracy, precision, recall, and F1 score of the GBDT model were respectively 0.87, 0.84, 0.76, and 0.79. Conclusions: There is great potential for machine learning in mortality prediction for ICH patients in ICU. Considering the above five models, we believe that GBDT is an appropriate tool for clinicians to predict ICH patient mortality.