To address the limitations of conventional machining, non-conventional machining processes (e.g., laser machining , electrical discharge machining (EDM) , waterjet machining , as summarized in Fig. (d1-d3)) offer distinct advantages, particularly for the fabrication of intricate geometries and reducing the risk of material damage like delamination or fibre pull-out. However, to fully exploit their potential, further research is needed in predicting/optimizing these non-conventional machining processes through data-driven approaches, real-time monitoring and adaptive control. Unlike mechanical machining, each non-conventional process requires specific sensors for effective process monitoring: laser machining requires sensors for laser power, temperature, and gas pressure; EDM necessitates monitoring of discharge voltage, current and discharge energy; and waterjet machining depends on sensors for water pressure, flow rate, and nozzle distance. Currently, the work on data-driven prediction and optimization of non-conventional machining of CFRP is very limited.