Thermal conductivity is a fundamental engineering property governing heat transfer process in soils. It depends on mineral component, compaction moisture content, dry density, soil gradation and temperature, and usually varies over an order of magnitude in soils with different status. The present study investigated the mechanism of heat transfer in soils and developed new models for thermal conductivity prediction via artificial neural network (ANN) technology. The performance of the proposed models (individual ANN model and generalized ANN model) were evaluated by comparing with three empirical models. Based on the results presented in this study, it is revealed that heat flow through soil was a multi-field coupled process (i.e., thermal-hydro-mechanical process) and was closely related to the intrinsic properties of three phases those constitute a soil. The cross validation was conducted to validate the reliability of the proposed models. It was concluded that both individual and generalized ANN models were able to provide good matching with laboratory measured thermal conductivity values. For each proposed model, the coefficient of correlation (R²) and variance account for (VAF) values were close to 1, and the mean absolute error (MAE) and root mean square error (RMSE) were lower than 0.360 W/K m and 1.000 W/K m, respectively. Results taken from the comparison of various models showed that the generalized ANN model, with RMSE value lower than 0.100 W/K m, exhibited highest accuracy in thermal conductivity prediction of all types of soil, followed by individual ANN models and the empirical models. A good performance of the proposed models in frozen soils was observed with a limited size of thermal conductivity data.