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One of the important steps taken within the scope of Industry 4.0 is predictive maintenance studies. In this way, machine and equipment life is extended and working efficiency is increased. Predictive maintenance studies of aircraft and aircraft engines are important, especially since flight safety is of vital importance in the aviation industry. In this study, 10 different machine learning algorithms were used to predict the remaining useful life of turbofan engines and their results were compared. For the trainings, the CMAPSS dataset, which shows the working conditions of turbofan engines under certain conditions, presented by NASA was used. The results of the study show that machine learning models generally achieve similar performance to each other. As a result of the experiments, it was seen that the LDA algorithm was the most successful algorithm. [ABSTRACT FROM AUTHOR]