The identification of different meat cuts for labeling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for not only error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images, but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to integrate structured and unstructured data to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. The dataset for one of the products exhibited sparsity, resulting in an imbalanced distribution. To rectify this issue, image augmentation techniques were employed to tackle the inherent imbalance within the dataset. Individual cut images and weights from 7,987 meats cuts extracted from semimembranosus muscles (i.e., Topside muscle), post editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions), precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of classical neural networks including convolutional neural network and residual network. The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires Integrating Structured and Unstructured Data for Imbalanced Classification Using Meat-Cut Images relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species such as lamb, chicken, or pork.