Abstract: Energy conservation, emission reduction and consumption reduction in long-distance oil and gas pipeline systems are important means for China Oil & Gas Pipeline Network Corporation to achieve the "carbon peaking and carbon neutrality" goals. Therefore, it is necessary to study energy-saving and consumption-reducing optimization technologies for natural gas pipeline networks. Based on this, the analysis of daily gas consumption characteristics of natural gas pipeline network systems was conducted, along with the construction of feature engineering and the research on a daily gas consumption prediction model library based on artificial intelligence technologies, to achieve accurate predictions. Subsequently, an optimization model for the operation of natural gas pipeline network systems was constructed, with the objective function aimed at minimizing energy consumption in pipeline transportation. This model incorporated the gas consumption prediction results, employed hydraulic constraint linearization to reduce nonlinearity, and utilized genetic algorithms for optimization. Furthermore, taking a natural gas pipeline network system of China Oil & Gas Pipeline Network Corporation as a case study, energy-saving and consumption-reducing optimization technologies were applied in actual production. The optimization practices focused on improving the pipeline network flow distribution, unit configuration, and operational parameters. The results indicate that implementing these optimization technologies effectively reduces energy consumption costs, enhances the management of energy consumption in the production and operation of natural gas pipeline network systems, and supports China Oil & Gas Pipeline Network Corporation in achieving high-quality connotative development and the "carbon peaking and carbon neutrality" goals.