Abdominal aorta aneurysm is one of the harmful cardiovascular diseases which is characterized by a widening of the aorta, the body’s major artery, in the lower region of the body. The diagnosis and treatment of abdominal aortic aneurysms (AAAs) require precise segmentation due to the significant medical risk and demands for accurate segmentation for diagnosis and treatment. This study presents an enhanced method for AAA segmentation from medical imaging data that makes use of statistical techniques, where statistical algorithms offer distinct advantages over traditional methods due to their ability to iteratively refine estimates and address complex optimization challenges, specifically Expectation Maximization (EM) and Majorization-Minimization (MM). To enhance segmentation, EM refines estimates iteratively in order to maximize the likelihood function, whereas MM approaches large optimization problems by breaking them down into simpler subproblems, resulting in reliable performance. This study highlights the significant improvement in AAA segmentation accuracy that these statistical methods bring, leading to increased diagnosis accuracy and better patient outcomes. This study makes use of EM and MM and automates the segmentation process of AAA image, harnessing the capabilities of EM and MM algorithms. This research aims to significantly enhance segmentation accuracy and diagnostic precision, thus contributing to improved patient outcomes in modern healthcare practices. Additionally, this improves patient care by streamlining workflows in medical environments, which benefits the healthcare industry as overall.