By identifying these patterns, the new approach enables data-driven predictions and decisions, enhancing both the accuracy and efficiency of machining performance prediction and optimization. As illustrated in Fig. , existing research on the application of data-driven methods in CFRP machining process can be categorized into four key scenarios: I. Prediction of process characteristics and machining quality/defects II. Monitoring of tool condition and prediction of tool wear III. Recognition of material transition in CFRP/metal stack machining IV. Vision-based automatic recognition of machined surface defects To date, a large body of literature has been published on CFRP machining, with topical reviews covering various topics such as cutting force modelling , thermal effect , delamination/burr defects , cutting tool design , tool wear and process optimization . However, none of them has focused on the application of data-driven methods. Although several reviews explored application of data-driven method in CFRP manufacturing processes (e.g. forming , automated fibre placement (AFP) , curing , additive manufacturing ), none has specifically focused on the machining process -a critical stage in CFRP's production cycle. This leaves a gap in understanding the role of data-driven method in CFRP machining process prediction and optimization. A comprehensive, up-to-date review in this aspect is therefore highly valuable and would highlight its important role within the CFRP manufacturing value chain.