Gamma Knife is an advanced form of radiation therapy allowing non-invasive tumor treatment by delivering high radiations doses to localized brain regions. Early detection of tumor recurrence after Gamma Knife radiation therapy can significantly influence treatment decisions and improve outcomes in brain cancer patients. In this work we propose several machine learning solutions to predict local recurrence of metastatic brain tumors after Gamma Knife radiation therapy exploiting MRI scans, radiation dose maps and clinical patient information. The training set consists of 140 stable and 13 recurrent lesions, while the test set contains 81 stable and 10 recurrent lesions. Oversampling, focal loss, data augmentation and thresholding are adopted to face data imbalance along with cross validation to improve model generalization.