Differentiation of brain glioma and solitary metastasis is clinically crucial for prescribing the patients’ management and assessing the prognosis. However, indistinguishable signs between two tumors on conventional MRI always embarrass the radiologists and thus lead to high misdiagnosis rate. To address such issue, series of MR features like grey level co-occurrence matrix, histograms of oriented gradient, shape and etc. were first extracted to detail the tumors’ histologic and morphologic characteristics. Then, a gradient-boosting machine learning approach was employed to distinguish the two tumors by the MR features. A good performance with area under receiver operating characteristic curve 0.80, sensitivity 85% and specificity 78% was obtained, suggesting the potential role of our approach in identifying brain glioma and solitary metastasis.