In general, gas turbine reliability is a challenging subject that is influenced by several aspects such as design, maintenance, operating conditions, and ecological issues. Gas turbines are widely used in power generation, aviation, and industrial applications, and reliability is crucial in all of these applications. Gas turbine reliability is measured using a variety of strategies, including mean time between failures (MTBF), mean time to repair (MTTR), availability, and reliability increase. These measurements may be used to track gas turbine performance over time and suggest areas for improvement in design or maintenance procedures. The main objective of this work is to fit the Johnson SU distribution to gas turbine data reliability. Based on the parameters estimations of the Johnson SU function, the survival function is calculated and used as a lifetime distribution model to estimate the failure rate or MTBF and anticipate the reliability of components or systems under operating conditions. As a result, it is critical to do adequate checks and sensitivity assessments to guarantee that the results are accurate and trustworthy. The results obtained can be used to determine the goodness of fit to our specific data after analysis. Finally, the Johnson SU distribution is commonly accepted as a model for turbine reliability data.