Background: Many issues arise from meat adulteration with regard to food safety and halal certification. Because pork is so visually similar to beef, removing or identifying either type of product becomes almost impossible without some sort of testing. Unfortunately, for many situations, traditional methods for testing authenticity are typically lengthy, costly, and impractical. Objective: This study utilizes computer vision to authenticate beef, pork, and mixtures of both using a deep learning approach. The method used consisted of preprocessing the photographs and performing various forms of data augmentation to increase the dataset’s representative and generalizing capabilities. Methods: We achieved our highest classification accuracy through transfer learning using ResNet-50 as the primary model used for classifying the different types of meats covered by the study, providing empirical metrics and K-fold cross-validation to support reliable classification results. A comparative evaluation using MobileNetV2, EfficientNetB0, and InceptionV3 was also conducted to validate the effectiveness of the proposed ResNet 50 model. Results: The experiment showed that the proposed ResNet-50 model could classify images into their respective classes with a high level of accuracy (98.33%) and greater than 97% for both precision and recall across all classes, in addition to an average inference time of 0.124 s per image. Furthermore, the 5-fold cross-validation yielded an average classification accuracy of 99.33% ± 0.37%, indicating that the performance was stable across multiple data partitions. The low inference time demonstrates the feasibility of the proposed model for real-time meat authentication applications on mobile devices. Conclusion: Overall, the findings indicate that the proposed approach is effective for meat product verification and that computer vision solutions can support the development of practical food safety and halal verification applications. Keywords: Meat authentication, Deep learning, Convolutional Neural Network, ResNet-50, Real-time classification, Food safety