Ayzit, TolgaSahin, Onur GungorErol, SelcukBaba, Alper2026-01-252026-01-2520260375-65051879-3576https://doi.org/10.1016/j.geothermics.2025.103567https://hdl.handle.net/11147/18849Erol, Selcuk/0000-0002-1886-059X; Ayzit, Tolga/0000-0001-5710-0713; Şahin, Onur Güngör/0000-0002-0502-1033Thermal conductivity is a fundamental parameter that significantly influences the thermal regime of the lithosphere. It plays a crucial role in a variety of geological applications, including geothermal energy exploration, igneous system assessment, and tectonic modeling. In this study, a machine learning approach is used to predict the thermal conductivity of igneous rocks based on the composition of major oxides. A total of 488 samples from different regions of the world were analyzed. The thermal conductivity values ranged from 1.20 to 3.74 Wm(-1) K-1 and the mean value was 2.61 Wm(-1) K-1. The Random Forest (RF) algorithm was used, resulting in a high coefficient of determination (R-2 = 0.913 for training and R-2 = 0.794 for testing) and a root mean square error (RMSE) of 0.112 and 0.179, respectively. Significance analysis of the traits identified SiO2 (>40 %), Na2O (>15 %) and Al2O3 (>10 %) as the most influential predictors. The study presented results from the Western Anatolia region, where felsic rocks had the highest thermal conductivity (mean = 2.69 Wm(-)(1)K(-)(1)) compared to mafic (mean = 2.34 Wm(-)(1)K(-)(1)) and ultramafic rocks (mean = 2.39 Wm(-)(1)K(-)(1)). In addition, the study evaluated the predictive capabilities of machine learning models for the igneous rocks of the Dikili-Bergama region and compared the results with those of saturated models. Using these data, we calculated heat flow values of up to 400 mWm(-2) under saturated conditions in western Anatolia. These results highlight the value of integrating geochemical data with machine learning to improve geothermal resource exploration and lithospheric modeling.eninfo:eu-repo/semantics/closedAccessGeothermal ExplorationMachine LearningHeat FlowRandom ForestThermal ConductivityWestern AnatoliaArticle2-s2.0-10502568849010.1016/j.geothermics.2025.103567