Machine learning is a science that deals with the design and development processes of algorithms that enable data-based learning. Machine learning methods try to find the most suitable model for new data prediction processes by using the past data. In this study, the data obtained from the engine trials with fuel mixtures of 5%, 10%, 15% by volume using 1-Propanol, 2-Propanol, AVGAS and gasoline fuel were used. Obtained data were compared with 100% gasoline values. In the study, a 4-cylinder engine with direct injection and turbocharging was used. With the obtained measurement results, a database was created to be used in machine learning. With the created database, estimation processes were carried out on ANN, GBA, SVM and AB machine learning models. At the end of the study, it was found that the most suitable model for the estimation of CO, CO2, HC, O2 values was ANN with an R 2 value of 0.9999. For the NO value, it was determined that the AB method was used with an R 2 value of 0.9996. In the estimation process of the CO value, GBA and AB methods are other machine learning methods that can be used as they have a higher value than 0.99 R 2 . CO2, HC and O2, and in the output value estimation process, GBA and AB are other methods that can be used instead of ANN as they have a higher value than 0.99 R 2 . It has been found that there is another machine learning method that can be used for NO value estimation, with an AB 0.99 R 2 value.