• For the prediction of process characteristics and machining quality/defects, data-driven models typically utilize predefined machining conditions as input. These inputs include cutting parameters (e.g. spindle speed, feed rate, depth of cut), cutting tool (geometry, material, coating), CFRP material composition (fibre/matrix, stacking sequence, fibre volume fraction) and coolant conditions. The datadriven model can predict outputs such as process characteristics (force, temperature, energy consumption, vibration etc.) and machining quality/defects indices (delamination, burr, surface roughness). Both machine learning and deep learning algorithms have exhibited good prediction accuracy. However, the training dataset is often obtained from costly experiment, and its size is often less than 100, reflecting severe data scarcity. This should be addressed in future work to further enhance prediction accuracy.