For data-driven CFRP machining prediction and optimization, ensuring timely responses while avoiding delays presents significant challenges, including the need for substantial hardware investment and issues like dust and mechanical shock. A promising solution is the integration of Cloud Computing, which can reduce reliance on local computational resources and offer scalable, flexible computation. Key areas of investigation should include the development of congestion control algorithms (e.g., Exponential Backoff) to manage request congestion, and the implementation of a producer-consumer pipeline with a priority queue to handle scheduling congestion . Performance evaluation in future work should emphasize key metrics, such as system latency, computational scalability, and recognition accuracy, ensuring that the framework can meet the real-time requirements of CFRP machining applications. Internet of Things (IoT) sensors could facilitate real-time data collection on key process parameters. Integrating these technologies into a closed-loop control framework would allow adaptive adjustments to machining parameters in response to dynamic process changes, ultimately enhancing precision, reducing downtime, and minimizing material waste. Such a system would represent a significant advancement in CFRP machining, driving the field towards more sustainable and efficient manufacturing practices.