The abstract introduces a novel multimodal approach for the early detection of dementia through the utilization of deep learning techniques. By combining a variety of data sources such as neuroimaging, genetic information, cognitive assessments, and clinical data, the proposed method aims to enhance the accuracy and reliability of dementia diagnosis at an early stage. Deep learning models, specifically designed for multimodal data analysis, are employed to extract complex patterns and relationships from the heterogeneous dataset. The integration of these diverse data modalities enables a comprehensive understanding of the disease progression and facilitates the identification of subtle biomarkers that may indicate the onset of dementia. Through the development of advanced deep learning algorithms, this approach demonstrates promising results in terms of predictive performance and generalization capabilities. The proposed multimodal framework provides a strong foundation for future research in the field of dementia diagnosis and offers insights into the potential use of cutting-edge technologies for improving early detection and intervention strategies. Keywords: Multimodal approach, early detection, dementia, deep learning, neuroimaging, biomarkers, predictive performance, intervention.