The aim of this study is to provide insights into the performance of copper-based brake pads used in high-speed trains and contribute to a more predictable braking system by leveraging mathematical and artificial intelligence (AI) models. The wear behavior of Cu-based brake pads in high-speed trains was investigated using a pin-on-disc test setup under different speeds, temperatures, and loads with a constant sliding distance. Additionally, mathematical and AI models were developed to predict the friction coefficient and wear rate values obtained from the experiments. This innovative approach initiates a significant discussion in line with a current need, and the sharing and publication of the obtained results are currently essential to address the knowledge gap in this field. The results revealed that an increase in temperature led to an increase in both the friction coefficient and wear rate. Conversely, an increase in load resulted in a decrease in both the friction coefficient and wear rate. The transition from abrasive wear to adhesive wear occurred due to the softening of copper between friction surfaces, leading to material transfer. According to the results obtained from the models, both the artificial neural network (ANN) and multiple regression models demonstrated comparable accuracy, predicting the friction coefficient with approximately 94% accuracy in both cases, indicating reliable predictions. For the wear rate, the models achieved approximately 90% and 92% accuracy, respectively.