This paper considers the opportunity to estimate the power of gas turbine power for natural gas transportation, using linear machine learning methods. Source data was used to archive gas-dynamic parameters from the automatic control system of the gas turbine. The method is based on changing the enthalpy of the natural gas before and after the centrifugal gas compressor is used for creating a dataset with measured parameters and power from the gas turbine. The software is implemented using Python and the Scikit-learn library is used to create machine learning models. A mean average percentile error is chosen as the model quality criterion. In this paper, different sets of feature parameters are researched by the quality of the prediction machine learning models. Recommendations on the use of models are given and the error of the method is determined. The hypothesis about the use of models trained on the data of one engine to estimate the power of other engines of the same type is refuted.