The neurodegenerative condition Alzheimer's disease (AD) has phenotypic alterations linked to genetic variations and imaging pathology. AD costs about $250 billion annually and affects 5.8 million Americans. Biomarker genomics, based on brain imaging, has been created to better the AD pathogenesis and early diagnosis. Therefore, effective ways to find AD biomarkers for early diagnosis and medication are needed. Conventional diagnostic methods, focused on postmortem findings and clinical symptoms, struggle to understand genetic, molecular, and neuroanatomical interactions. This noninvasive, high-throughput technique addresses these issues by combining genomic data with multimodal imaging phenotypes. The proposed framework introduces Multimodal Alzheimer’s Disease Diagnostic model (MADM) which integrates Neuroimaging, cognitive assessments, and genetic data to enhance early detection, improve diagnostic accuracy, and personalize treatment strategies. The MADM system analyzes multimodal data, finds illness biomarkers, and predicts progression using powerful AI and ML. This paradigm's thorough and systematic Alzheimer's disease diagnosis includes cognitive test scores, MRI, PET, and genetic indicators including APOE and SNP genotyping. Consolidating data sets is key to precision medicine. This enables targeted therapy and better patient outcomes. Multimodal dementia ratings outperformed MRI and PET scan dementia ratings in predicting MADM progression in healthy control participants, according to ADNI data. In contrast to relying just on genetic data, a combination of neuroimaging, cognitive test scores, and genetic markers was found to be a more effective way to detect patients with stable moderate cognitive impairment. Classification accuracy in the remaining stratified groups was enhanced by combining multimodal data.