Accurate thermal prediction of gas turbine blades is essential to ensure successful operation throughout the design life. Large Gas turbines operate in different conditions based on customer requirements, due to which turbine blades are subjected to variations in thermal loading conditions. Simulating this behavior using conventional finite element modeling involves detailed and time-consuming analyses for calculation of blade temperature, which can be further utilized to assess cyclic and creep life. This paper deals with developing and utilizing machine learning based surrogate models to predict the sectional temperature (output) of a radially cooled blade. The surrogate models are developed to predict the output using turbine inlet temperature, hot gas mass flow, cooling air temperature and cooling air mass flow as input to the machine learning (ML) model. All thermal parameters for ML model have been obtained from CFD based 3D thermal calculations. A comparative study is presented between linear regression, decision tree, random forest, and gradient boost ML models, to select the model with the least mean absolute error. Additionally, hyperparameter optimization is performed using grid search to minimize the error. The results show that the linear regression-based model outputs the least mean absolute error of 6.5°C and the highest dependence of the output is on the turbine inlet temperature, followed by the cooling air temperature. The findings show a good agreement between the predicted output of the surrogate model and multi-dimensional physics based thermal calculations, while offering a considerate reduction in analysis time.