This study is to identify the optimum prognosis index for brain metastases by machine learning. Seven hundred cancer patients with brain metastases were enrolled and divided into 446 training and 254 testing cohorts. Seven features and seven prediction methods are selected to evaluate the performance of cancer prognosis for each patient. We used mutual information and rough set of particle swarm optimization (MIRSPSO) methods to predict the patient prognosis with the highest accuracy at AUC=0.978 ± 0.06). The improvement by MIRSPSO in terms of AUC was at 1.72%, 1.29% and 1.83% higher than that of SFS, MIPSO, and MISFS respectively. Furthermore, the clinical performance of the best prognosis was superior to conventional statistic method in accuracy, sensitivity, and specificity. In conclusion, identifying optimal machine-learning methods for the prediction of overall survival in brain metastases is essential for clinical applications. The accuracy rate by machine-learning is far higher than that of conventional statistic methods.