The disruption of specific microstructural features of white-matter (WM) has been observed in methamphetamine (MA) abusers. However, it remains unknown whether WM volume is abnormal in MA abusers. To address this issue, a machine learning approach was applied in this study to differentiate between 21 MA abusers and 13 age- and gender- healthy controls. Our results showed that a linear support vector machine classifier achieved an accuracy of 73.53% using the white matter volume as input features. Particularly, the most discriminative WM regions included pontine crossing tract, motor system and the reading related network.