Aging of the human brain is marked by considerable variability, with specific regions such as the medial temporal lobe (MTL) showing increased vulnerability to neurodegeneration in disorders like Alzheimer’s disease (AD). Emerging evidence suggests that epigenetic mechanisms, particularly DNA methylation, may contribute to this regional susceptibility and offer potential as peripheral biomarkers of brain aging. In this dissertation, we investigate the hypothesis that blood-based epigenetic modifications reflect and predict patterns of brain aging and neurodegeneration, as measured by in vivo structural MRI. To address this, we pursue two aims using a combination of statistical modeling and machine learning (ML) techniques. In Aim 1, we assess whether biological aging—quantified via epigenetic clocks applied to blood-derived DNA methylation—explains individual differences in cortical and MTL neurodegeneration beyond chronological age. We introduce a novel imaging-derived biomarker, the Cortico-Medial Temporal (CoMeT) index, to capture regional atrophy along the cortical-MTL axis. Associations between CoMeT and epigenetic age acceleration are evaluated through hypothesis testing and subgroup analyses, revealing that faster biological aging correlates with greater vulnerability in AD-sensitive brain regions. In Aim 2, we develop predictive models of brain age and hippocampal volume from genome-wide methylation data using Elastic Net regression. Within a nested cross-validation framework, we compare feature selection strategies and identify robust epigenetic signatures of neurodegeneration. These features are annotated and interpreted using gene ontology and enrichment analyses. We further assess the convergence of methylation markers across independent datasets and examine their correspondence between blood and brain, as well as associations with blood gene expression. Results indicate partial overlap in peripheral and central signatures, suggesting that while blood methylation captures biologically relevant information, it incompletely reflects brain-specific processes. Together, these studies demonstrate that epigenetic markers are associated with regional patterns of brain aging and neurodegeneration, and that blood-based methylation profiles offer promising, albeit limited, surrogates for brain imaging phenotypes. This work provides new insights into the molecular correlates of neurodegeneration and highlights the potential of epigenetics for advancing early detection and mechanistic understanding of age-related cognitive decline.