Traditional methods of meat quality assessment face limitations in addressing the complex demands of modern food safety and nutritional standards. To overcome these challenges, researchers have increasingly introduced advanced technologies such as electronic noses and electronic tongues. These technologies, known for their speed and cost-effectiveness, have shown promising results in the qualitative analysis of meat products, but they are difficult to meet the demands for higher precision. In contrast, artificial neural networks (ANN), with their powerful nonlinear mapping capabilities, have demonstrated significant advantages in the field of food quality assessment, especially in meat quality evaluation and non-destructive testing. The classic application architectures of ANNs are reviewed in meat quality evaluation and their practical applications in various meat products are systematically summarized, including fresh meat, fish, shrimp, and processed meats. Future research may focus on developing more precise predictive models, achieving real-time monitoring, and employing multi-model fusion techniques to further enhance the intelligence of meat product quality assessment.