Breast cancer is the second leading cause of cancer death among women in the US. Recognizing the complexity of cancerous tissue, several non-Gaussian diffusion MRI models, such as the continuous-time random-walk (CTRW) model, were suggested to probe the underlying tissue environment. In this study, we employed a support-vector-machine-based analysis on the histogram features of CTRW model parameters to differentiate malignant and benign breast lesions. This multi-parameter multi-feature approach provided the best diagnostic performance compared to the conventional single-parameter or single-feature analysis techniques. The combination of machine-learning with non-Gaussian diffusion MRI can facilitate comparable diagnostic performance to that of dynamic-contrast-enhanced MRI.