Meat is a vital source of protein, vitamins, and minerals, including iron, zinc, selenium, and B vitamins. Improving meat shelf life is crucial for food industry, at the same time maintaining a healthy lifestyle is the need of the hour in this commercialized world. A study focuses on classifying and identifying meat quality using computer vision tasks, mainly focusing on automated classification of meat images into three categories: Fresh Meat, Half-Spoiled Meat, and Spoiled Meat. A dataset of 2268 meat images is used, with 1816 images for training and 452 images for testing. Four pretrained deep learning models, VGG19, ResNet50, MobileNetV2, and InceptionV3, are used to extract features and classify meat images based on their freshness status. The performance of the model is evaluated using metrics like precision, recall, and F1-score and good level of accuracy obtained such as 86.2% of accuracy in VGG19, 90.3% of accuracy in InceptionV3 and 92.6% in ResNet50, and 95.1% of accuracy in MobileNetV2. The experimental results show the effectiveness of deep learning models in accurately categorizing meat, with potential applications in enhancing quality assurance processes in the food industry. The findings underscore the importance of deep learning approaches in streamlining meat classification processes and improving overall efficiency.