Tau protein accumulation is a hallmark of Alzheimer's disease (AD), playing a crucial role in neurodegeneration and cognitive decline. The visualization of tau deposition in the brain through tau-PET imaging is invaluable for the diagnosis and monitoring of AD. However, tau-PET imaging is not widely accessible due to its high cost, limited availability of tracers, and the need for specialized equipment. In contrast, blood biomarkers offer a more practical and less invasive alternative for disease monitoring. Recent research has demonstrated a significant relationship between blood biomarkers and tau-PET standardized uptake value ratio (SUVR), highlighting the potential of blood-based diagnostics. Our team has successfully used blood and cerebrospinal fluid (CSF) biomarkers, specifically phosphorylated tau (p-tau217 and p-tau181), along with measures of regional brain atrophy, to predict and enhance the accuracy of regional tau-PET SUVR. The primary aim of this project is to develop a robust methodology to generate tau-PET images from blood biomarkers, thereby facilitating easier and more accessible diagnosis of AD. By integrating advanced generative AI algorithms with multimodal data, including blood and CSF biomarkers as well as neuroimaging markers, we aspire to create a reliable and cost-effective tool for clinicians. This project promises to bridge the gap between the need for precise tau imaging and the limitations of current diagnostic practices, ultimately contributing to better patient outcomes and advancing our understanding of Alzheimer's disease.