Due to its geographical conditions, our country regularly faces the reality of natural disasters, especially earthquakes. Considering that most of the loss of life and property is caused by earthquakes and this region is shaken by a devastating earthquake in an average of five years, earthquake disaster comes first in terms of precautions to be taken. It is of great importance to be able to predict earthquakes in order to determine the precautions that can be taken for earthquakes. In this context, earthquake prediction studies with machine learning have gained momentum in recent years. In this study, earthquake predictions were made using an earthquake catalog and a data set combining geological and geodetic data. This data set is divided into test and training data in order to train the algorithm models used in the study and to measure the performance of the trained models. By using random forest, extreme gradient boosting, decision tree and k nearest neighbor regression algorithms, the models were trained with the training set and the trained models were tested with the test data. Analysis results were compared and evaluated. According to the analysis results, random forest and extreme gradient increment regression algorithms were the algorithms with the most successful results. When the mean square error (MSE) values are examined, the best results are observed in the data set consisting of earthquake information, strains and fault information. This study contributed to the literature by bringing a different perspective to the data set used in earthquake prediction studies with machine learning. © 2023, Gumushane University. All rights reserved.