Abstract: Under the context of intelligent coal mine development, this study reviews the research progress in optimizing the performance of gas extraction pipeline network and intelligent control for promoting efficient and intelligent gas extraction. Specifically, a composition framework for gas extraction pipeline network was established, and the fluid flow laws within the pipeline network were elucidated. A "static-dynamic" two-dimensional framework was established based on the existing performance optimization technologies for gas extraction pipelines. The static optimization technologies can enhance the system's inherent performance through pipe material selection, structural redesign, and fault diagnosis, while the dynamic optimization technologies can dynamically adjust pump and valve parameters based on multi-source data sensing and intelligent evaluation to ensure real-time optimal operation of the pipeline network system. The present performance optimization and intelligent control technologies face such limitations as the lack of a universal model for pipeline network topology design, insufficient visualization and verification of abnormal conditions, and weak coordination among intelligent algorithms. To address these challenges, this study proposed to determine optimal combinations of parameters such as pipe diameter and slope by combining orthogonal experiments with the Analytic Hierarchy Process; develop a testing platform for simulating abnormal operations and control of mine gas extraction pipeline to replicate operating conditions such as leaks, blockages, and deformation; integrate various data mining and neural network algorithms to establish more effective intelligent control models. This study can provide guidance and reference for promoting intelligent and efficient coalbed methane extraction and ensuring the safe and sustainable development of coal mines.