A method for estimating the technical condition coefficient of a gas turbine unit for transporting of natural gas using machine learning methods is considered. Archival gas-dynamic parameters recorded by the installation's automatic control system are used as initial data. The initial data set was created by determining power from the change in enthalpy of natural gas before and after the blower. The software package is implemented in Python; the Scikit-learn library is used for machine learning models. The average absolute error in percentage is chosen as a criterion for the quality of the prediction. The prediction quality of machine learning models is assessed for different sets of parameters-features and sample sizes. Recommendations on the use of models are given.