A new data-driven approach to interpreting the nonlinear problem of total desorbed gas content analysis of coal seams is presented in this study. The study focuses on a low-rank coal reserve located in the Kınık coalfield, which was investigated using the United States Bureau of Mines (USBM) direct desorption method to anticipate the total desorbed gas content of coal seams for underground mining operations. The core samples collected during the reserve and gas content analysis were used to feed machine learning models, which were trained using coal properties data such as depth, moisture, ash, volatile matter, and calorific value, in relation to total desorbed gas content. Multiple linear regression, support vector machine, and artificial neural network were employed to predict the total desorbed gas content of coal seams in the Kınık coalfield. The machine learning models were optimized using hyperparameter tuning, and the most successful model was selected based on its regression and computational cost performance. Sensitivity analysis was conducted to investigate the performance of the coal properties on total desorbed gas content. The selected model was then utilized for predicting the total desorbed gas content of coal seams at a single point in the coalfield. The findings of this study provide insights and guidelines for unconventional reservoir analysis and petrophysical system prediction using machine learning methods. Overall, this study demonstrates the potential of machine learning in addressing nonlinear problems in the field of geology and provides a promising approach for future research in this area.