A rising worldwide health burden, cognitive diseases such as the Alzheimer disease, the Parkinson disease with dementia, the vascular dementia and the front temporal dementia lead to a progressive memory, reasoning and functional independence. Large and varied datasets are becoming more and more important in this field of study in order to support predictive modeling, therapeutic development, and early diagnosis. However, the research utility of real-world clinical data is limited because these datasets are frequently fragmented and imbalanced, and privacy regulations. Synthetic data generation can be a promising solution as it can be used to create scalable and statistically representative, privacy-preserving datasets to complement Regular clinical sources. This paper discusses the principles, the forms, and methods of data generation including statistical methods, machine learning, deep learning, and simulation-based approaches of synthetic data generation. Some of the applications analyzed in this discussion include neuroimaging, clinical data modelling, speech and language analysis, EEG research, and multimodal integration. It deals with the issues of data fidelity, amplification of bias, and regulatory uncertainty, and also provides some benefits, such as improved access to data, increased equity, reduced costs, and faster development of AI models. In the next step, the paper suggests applying federated learning and digital twin technologies and large language models to enhance the usefulness of synthetic data in re-search of cognitive diseases and personalized medicine even more.