The rest of this article is organized as follows: Section 2 give a high level summary on fundamentals of CFRP machining process (cutting force, temperature, delamination, burr surface roughness and tool wear). Section 3 gives an overview on the commonly used artificial intelligence (AI) algorithms (including machine learning and deep learning) and their general rules. Section 4 details the current application of data-driven method in CFRP machining for four typical scenarios (as shown in Fig. ): (i) prediction of process characteristics and quality/defects, (ii) tool condition monitoring and tool wear prediction, (iii) recognizing material transition in CFRP/metal stack machining and (iv) machine-vision based recognition of surface defects. Section 5 reviews the process optimization methods (single/multi-objective optimization) for optimizing CFRP machining process.