SVR constructs a decision boundary that maximizes the margin between data points and control overfitting through regularization, enabling accurate predictions even with limited data. However, to the author's best knowledge, there are only few notable studies have successfully employed these small-sampleprediction methods in CFRP machining. Future work should further explore the application of small-sampleprediction methods like GPR and SVR and their integration with other machine learning techniques, such as ensemble learning or hybrid models, to address the data scarcity problem.