• Machining of CFRP presents significant challenges, including surface defects, rapid tool wear, and material transition issues when drilling CFRP/metal stacks. These challenges highlight the need for effective, automated process prediction and optimization. Conventional physics-based models often rely on extensive computational resources and struggle to capture the complex dynamics of CFRP machining, especially with thermo-mechanical coupling loads and process uncertainties. To address this challenge, recent advancements in artificial intelligence (AI) offer a promising, data-driven solutions to replace conventional analytical/numerical models. Data-driven methods can reduce the need for extensive domain-specific knowledge and enable fast and accurate predictions by uncovering patterns within datasets.