The identification of cattle breeds is an important aspect of livestock management, particularly for maintaining genetic resources and supporting breeding programs of local cattle. In Indonesia, Aceh cattle represent one of the important indigenous breeds whose identification is commonly conducted through manual observation based on physical characteristics. However, conventional identification methods often depend on human expertise and may lead to inconsistencies or misclassification. Recent advances in artificial intelligence, especially in computer vision technologies, provide new opportunities to develop automated systems for livestock identification. This study aims to develop an image-based classification model to distinguish Aceh cattle from non-Aceh cattle using computer vision techniques. A dataset of cattle images was collected from field documentation and various online sources and categorized into two classes. After the image collection process was completed, image adjustment and augmentation processes followed, resulting in a final dataset of 2,360 images, which were used for model training and testing. The dataset consisted of 800 original images expanded through augmentation techniques and was automatically divided into training and validation datasets using an 80:20 ratio. The classification model was developed using Teachable Machine and evaluated using performance metrics such as accuracy, precision, and recall. The experimental results show that the model achieved an accuracy of 89.6%, with precision and recall values of 89.1% and 90.3%, respectively. The findings demonstrate the feasibility of applying low-code artificial intelligence platforms for indigenous cattle breed classification in digital livestock management systems.