Primary Progressive Aphasia (PPA) is a neurodegenerative disorder characterized by a gradual decline in language abilities, caused by localized atrophy in specific cortical regions. It manifests in three variants: non-fluent (nfvPPA), semantic (svPPA), and logopenic (lvPPA), each associated with distinct linguistic profiles and atrophy patterns. Standardized language batteries, together with structural Magnetic Resonance Imaging (MRI) quantification of atrophy, remain mainstays of clinical diagnosis. This thesis developed deep learning models for the automatic classification of PPA variants by integrating two clinical modalities: voice recordings collected during clinician-administered cognitive tests and volumetric three-dimensional (3D) T1-weighted structural MRI. For the audio stream, multiple network architectures were designed and compared to identify the most effective model for PPA discrimination.