In the past two decades, machine learning techniques have been extensively applied for the detection of neurologic or neuropsychiatric disorders, especially Alzheimer’s disease (AD) and its prodrome, mild cognitive impairment (MCI). This chapter presents some of the latest developments in the application of machine learning techniques to AD and MCI diagnosis and prognosis. We will divide our discussion into two parts: single modality and multimodality approaches. We will discuss how various biomarkers as well as connectivity networks can be extracted from the various modalities, such as structural T1-weighted imaging, diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI), for effective diagnosis and prognosis. We will further demonstrate how these modalities can be fused for further performance improvement.