The prediction of Abdominal Aortic Aneurysm (AAA) develpoment is crucial to early treatment and surgical intervention. It is critically important to capture key vascular growth factors like blood flow and intraluminal thrombus (ILT) accumulation in order to understand how vascular adaptation occurs. In this paper a novel has been proposed for detecting the AAA. Initially the input CT images are pre-processed using AUG filter to enhance the quality of the images. Then the enhanced images are fed into Modified Deep V3+ for segmenting the required features from the image. The segmented images are inputted into feature extraction process for extracting the required features using Effective Net. Finally, the normal and abnormal classes of the aortic aneurysm detection is classified using DBN classifier. The proposed archives an accuracy rate of 99.34% in the normal classes and 99.28% in the abnormal classes. The accuracy rating of 99.31% achieved by the suggested is higher than that of the current techniques. In this, the proposed achieves an overall accuracy rate of 0.77%, 0.31% and 1.17% over the existing methods such as CTA, PRAEVAorta and ATAAs respectively.