Deep Learning (DL) architectures proposed over recent years have been successfully applied to human data (such as medical data or facial data) to estimate age with reasonably good accuracy. However, the reasoning underlying the models' age predictions are still obscure. To address this challenge, Explainable AI (XAI) methods such as Deep Visualization (DV) have been applied to the deep learning models. Existing literature in the DV domain mainly considers image classification and object detection tasks involving two-dimensional input images. In this work, we show how DV can be applied to models handling high-dimensional medical data, and thereby identify a medical condition in a subject. The objective of this thesis is to perform a comparative study of the state-of-the-art DV techniques and adapt them for age estimation models. We use an iterative training framework to estimate the Biological Age (BA) from human brain MRI trained over a sizeable dataset across various age groups and Clinical Dementia Ratings (CDRs). We identify salient regions that affect aging by visualizing the gray matter volumes of the subjects. Further, we combine fine-grained and coarse-grained visualization techniques namely, Saliency Map Order Equivalent (SMOE) Scale saliency maps and GradCAM++ respectively to generate improved visualization maps. The differences in salient regions are observed across subjects with varying CDRs. The regions in the brain significantly affected by Alzheimer's Disease (AD) are identified based on the relative intensities in the visualization maps between healthy subjects and potential AD patients. In this work, we analyze how the application of Deep Visualization techniques bolster the theory of accelerated aging of the brain in the subjects having ailments, thereby assisting in timely diagnosis.