As machine learning continues to gain momentum in the neuroscience community, we witness the emergence of novel applications such as diagnostics, characterization, and treatment outcome prediction for psychiatric and neurological disorders, for instance, epilepsy and depression. Systematic research into these mental disorders increasingly involves drawing clinical conclusions on the basis of data-driven approaches; to this end, structural and functional neuroimaging serve as key source modalities. Identification of informative neuroimaging markers requires establishing a comprehensive preparation pipeline for data which may be severely corrupted by artifactual signal fluctuations. We propose a new unified data analysis pipeline for neuroimaging-based diagnostic classification problems using various different feature extraction techniques, Machine Learning algorithms and processing toolboxes for brain imaging. We illustrate the approach by discovering potential candidates for new biomarkers for diagnostics of epilepsy and depression presence in simple and complex cases based on clinical and MRI data for patients and healthy volunteers. We also demonstrate that the proposed pipeline in many classification tasks provides better performance than conventional ones.