Catching Parkinson’s and Alzheimer’s disease early is incredibly hard because of the symptoms creep up so slowly memory lapses that seem like normal aging, slight tremors that could be stress or fatigue. Generative AI Models are starting to spot the subtle patterns that human eyes might miss, potentially catching these diseases months or even years earlier than traditional methods. These systems are helping clinicians identify relevant early warning signs sooner, improve diagnosis accuracy, and address the small datasets that have been in the way of cognitive disease research for many years. The use of generative models such as diffusion models, variational autoencoders (VAEs), and generative adversarial networks (GANs) in neuroimaging for cognitive impairment has played an important role. Despite the potentially limited datasets, all of these approaches allow robust AI-driven diagnostics through the use of synthetic medical images, super-resolution reconstruction, anomaly detection, and modelling disease progression framework into disease that employee generative models are an innovative framework for obtaining the initial, scalable, and non-invasive cognitive disease diagnosis moving past traditional diagnostics that eliminate some of the current course limitations, facilitating precision medicine in neurology. Generative Models mitigate data sparsity and class imbalance by developing synthetic but authentic medical images, may augment any sparse set of available data, and deal with the heterogeneity in complex disease presentations in the analysis of brain image modalities (e.g., MRI, PET, and CT). The chapter has focused on application of generative models likes GAN, VAE and Diffusion models in medical imaging (MRI/PET/CT images) for cognition disease detection, Clinical implications, challenges and case studies, ethical reflection and quantum generative models (next-gen. computer). Technical challenges such as model interpretability, generalizability, as well as ethics, such as bias prevention and data privacy, are also critically evaluated.