By comparing the performance of artificial neural networks and traditional methods in predicting the strength of metal-based carbon fiber reinforced polymer layers, this study aims to evaluate the application potential of artificial neural networks in this field. By introducing the importance of strength prediction of metal-based carbon fiber reinforced polymer layers and the limitations of traditional methods in this field, the artificial neural network model used and the implementation steps of traditional methods are described in detail. In terms of research results, by comparing the decision coefficient (R2), it is found that the prediction model of artificial neural networks has better performance in explaining the strength of metal-based carbon fiber reinforced polymer layers. Compared with traditional methods, its prediction results are more accurate and have higher interpretation capabilities. In summary, the results of this study show that artificial neural networks have potential in the prediction of the strength of metal-based carbon fiber reinforced polymer layers, and can be used as an effective prediction tool in related fields.