Aging is a complex biological process that manifests across diverse anatomical scales, from molecules and cells to organs and organisms. Despite the significance of age-associated diseases in affecting quality of life and mortality, our understanding of how cellular-level changes result in tissue-specific loss of function remains incomplete. Several limitations persist: the inability of animal models to fully mirror human physiology, the restricted scope of longitudinal human studies to easily accessible organs, and a prevalent cell-centric focus that overlooks the broader microanatomical context. Additionally, the challenge of uncoupling aging from pathology is exacerbated by the absence of baselines defining healthy aging, hindering the accurate interpretation of alterations. To address these challenges, the proposed project leverages large-scale human tissue imaging datasets and machine learning techniques. Specifically, Aim 1 employs unsupervised learning to systemically quantify and categorize microanatomical structures across 40 human tissues. In a parallel stream, Aim 2 aims to detect and characterize the manifestation of age-associated pathologies in tissue, pinpointing the molecular changes associated with them, across spatial scales. Aim 3 integrates the insights from previous aims, seeking to identify how they contribute to the process of aging and onset of age-associated diseases. Ultimately, this will unearth early predictors of pathology, providing a transformative approach to disease detection and management. This project will not only offer a novel, comprehensive view of human tissue complexity in the context of aging but also challenge the traditional notion that age-associated pathology is merely an aggregation of cellular dysfunctions. By quantifying the interconnected aspects of tissue architecture changes with age, and the manifestation of pathology, we aim to redefine our understanding of the onset of age-associated diseases.