The rapid advancement of data-driven method along with machine learning / deep learning technology has catalysed a paradigm shift in the prediction and optimization of CFRP machining performance. With the integration of advanced sensors , vast amounts of data can be captured during the machining process, creating new opportunities to leverage its superior data-processing capabilities. The data-driven approach offers a promising solution to the complex, physics-based challenges inherent in CFRP machining. These methods typically require less domain-specific knowledge and can uncover underlying patterns within the dataset .