Abstract: Industrial machines positioned along a gas transmission pipeline are controlled using setpoints. Operators of such pipelines would benefit from real-time data and recommendations to guide them in optimizing performance of the pipelines. Accordingly, a compression optimization system is disclosed that monitors and provides real-time data and notifications, recommends optimal control setpoints using a machine-learning or other artificial-intelligence model, which may self-learn to realistically represent the pipeline, and executes simulations for hypothetical scenarios. The compression optimization system enables efficient management of the gas transmission pipeline.