Neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, pose significant diagnostic and therapeutic challenges due to their complex and heterogeneous progression patterns. Current monitoring methods rely on periodic clinical assessments and neuroimaging, which may not capture the dynamic changes in disease pathology. This paper presents a novel approach for real-time monitoring of neurodegenerative disease progression using generative adversarial networks (GANs). Our method leverages longitudinal neuroimaging data to train a GAN-based model that generates synthetic images of brain structure and function, simulating disease progression. By comparing generated images with real-time neuroimaging data, we can detect subtle changes in disease pathology and track progression in real-time. We evaluate our approach on a large dataset of neuroimaging studies and demonstrate its potential for early detection, disease staging, and treatment efficacy monitoring. Our results show that GAN-based real-time monitoring can improve diagnostic accuracy and enable personalized treatment strategies for neurodegenerative diseases.