Structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) based features toward identification of mild cognitive impairment (MCI) status has gained popularity due to its non-invasiveness allowing repeated measurements. Despite of this great potential, however an effort to integrate the sMRI- and fMRI-based features to increase MCI detection accuracy has been limited. In this study, we were motivated to investigate whether the detection capability of the MCI status can be improved via the integration of feature sets from sMRI and fMRI data. The characteristic traits of regional volumes and level of neuronal activity of the brain associated with the MCI in comparison to healthy control were exploited using sMRI and fMRI data, respectively, in which these characteristic traits (i.e., biomarkers) were identified from group comparison via two-sample t-test. In the subsequent classification phase, the MCI status were automatically detected using a support vector machine (SVM) algorithm employing the identified sMRI- and fMRI-driven biomarkers as input features vectors. The results indicate that the fMRI-based biomarkers appear to increase the detection accuracy of the MCI status than the sMRI-based biomarkers. Moreover, the integrated feature sets using the sMRI- and fMRI-based biomarkers constantly showed superior performance than the feature sets based on the fMRI-driven biomarkers. This study successfully demonstrated an anecdotal evidence that the integration of the sMRI and fMRI modalities can provide a supplemental information toward diagnosis of the MCI status compared to either the sMRI or fMRI modality.