Despite the large body of existing neuroimaging-based studies on brain dementia, in particular mild cognitive impairment (MCI), modeling and predicting the early dynamics of dementia onset and development in healthy brains is somewhat overlooked in the literature. The majority of computer-aided diagnosis tools developed for classifying healthy and demented brains mainly rely on either using single timepoint or longitudinal neuroimaging data. Longitudinal brain imaging data offer a larger time window to better capture subtle brain changes in early MCI development, and its utilization has been shown to improve classification and prediction results. However, typical longitudinal studies are challenged by a limited number of acquisition timepoints and the absence of inter-subject matching between timepoints. To address this limitation, we propose a novel framework that learns how to predict the developmental trajectory of a brain image from a single acquisition timepoint (i.e., baseline), while classifying the predicted trajectory as ‘healthy’ or ‘demented’. To do so, we first rigidly align all training images, then extract ‘landmark patches’ from training images. Next, to predict the patch-wise trajectory evolution from baseline patch, we propose two novel strategies. The first strategy learns in a supervised manner to select a few training atlas patches that best boost the classification accuracy of the target testing patch. The second strategy learns in an unsupervised manner to select the set of most similar training atlas patches to the target testing patch using multi-kernel patch manifold learning. Finally, we train a linear classifier for each predicted patch trajectory. To identify the final label of the target subject, we use majority voting to aggregate the labels assigned by our model to all landmark patches’ trajectories. Our image prediction model boosted the classification performance by 14% point without further leveraging any enhancing methods such as feature selection.