The aortic arch plays a vital role in the cardiovascular system, connecting the ascending and descending thoracic aorta and giving rise to three major arteries that supply oxygenated blood to the upper body and central nervous system. Its complex geometry and high inter-patient variability pose significant challenges for thoracic surgical planning and the design of endovascular devices. Despite its clinical importance, systematic characterization of aortic arch shape variation remains limited, particularly in cases involving the supra-aortic branches. This study addresses this gap by applying Statistical Shape Modeling (SSM), a data-driven method that captures anatomical variability with precision. The methodology includes data collection, geometric preprocessing, and shape registration, culminating in an interpretable statistical model of the aortic arch. Furthermore, the study explores how these models can be integrated into Computational Fluid Dynamics (CFD) simulations to assess the performance of branched stent grafts used in Thoracic Endovascular Aortic Repair (TEVAR). The aim is to evaluate their ability to restore physiological blood flow. Ultimately, this approach supports the development of more effective, patient-specific interventions.