The aorta plays a critical role in delivering oxygenated blood from the heart to the body. Accurate segmentation of aortic zones and branches in computed tomography angiography (CTA) is vital for diagnosis and surgical planning. We present our solution to the AortaSeg24 ( https://aortaseg24.grand-challenge.org/ ) challenge (Multi-Class Segmentation of Aortic Branches and Zones in CTA), which targets the automatic segmentation of aortic anatomy from 3D CTA volumes. We adopted the nnUNetv2 ( https://github.com/MIC-DKFZ/nnUNet/tree/master ) framework for its strong generalization in medical image segmentation, training our model on the provided dataset with minimal manual tuning. On the hidden test set of 40 cases, our method achieved an average Dice score of 0.729 \(\boldsymbol{\pm }\) 0.043 and an average normalized surface Dice (NSD) of 0.751 \(\boldsymbol{\pm }\) 0.047. High performance was observed in regions such as Zone 9 (Dice: 0.905 ± 0.048) and the Left Common Carotid Artery (Dice: 0.779 ± 0.094). All code used for training and evaluation has been made publicly available on GitHub ( https://github.com/puppy1234/Aortaseg24_source_code ). These results demonstrate the effectiveness of our nnUNetv2-based approach for large-scale aortic segmentation in clinical CTA data.