Our study aimed to create a machine learning model to predict patients’ functional outcomes after microsurgical treatment of unruptured intracranial aneurysms (UIA). Data on 615 microsurgically treated patients with UIA were collected retrospectively from the Electronic Health Records at N.N. Burdenko Neurosurgery Center (Moscow, Russia). The dichotomized modified Rankin Scale (mRS) at the discharge was used as a target variable. Several machine learning models were utilized: a random forest upon decision trees (RF), logistic regression (LR), support vector machine (SVM). The best result with F1-score metric = 0.904 was produced by the SVM model with a label-encode method. The predictive modeling based on machine learning might be promising as a decision support tool in intracranial aneurysm surgery.