In knowledge-based decision-support systems, soil temperature (ST) estimation can be considered as the core modeling task used for investigating the dynamics of solar energy exchange between the land surface and the sub-surface soil layers. In addition, the impacts of meteorological processes on energy uptake into the underlying soils and the design of more resilient and sustainable agricultural systems for improved crop health and resource management. In this paper, monthly ST estimation at various soil depths (i.e., 5, 50 and 100 cm) is performed by applying data-intelligent machine learning models: extreme learning machine (ELM), artificial neural network (ANN) and M5 Model Tree (M5 Tree). The predictive models are trained using meteorological information from two stations (i.e., Mersin and Adana, Turkey). The models are constructed using monthly input variables, including air temperature (T-air), windspeed (W), relative humidity (RH), solar radiation (SR), while the objective variable is the soil temperature measurement at 5, 50 and 100 cm depths for the period 1986–2010. Multiobjective performance criteria are applied to diagnose the predictive accuracy of the data-intelligent models, entailing the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), Willmott's Index (WI), Nash Sutcliffe's coefficient (Nash) and the Legates & McCabe's Index (LMI). Based on the tested dataset, the ELM model is yielded the most accurate performance for Mersin station compared to the lower performance of ANN and M5 Tree models. For this case, the most accurate performance is attained for the ST estimation at a depth of 50 cm, with the highest value of R=0.992, WI=0.999, Nash=0.981, LMI=0.879 and the lowest value of the relative RMSE and MAE values (i.e., 4.7 and 4.0%, respectively), attained using T-air, W, RH and periodicity as the predictor variables. The performance results for the Adana station behave differently, where ELM is seen to exceed t
