This study used the Gaussian Process Regression (GPR) method to predict the core losses of the Finite Element Analysis (FEA) based dry-type three-phase transformer. In the estimation and analysis processes, the core area A_c, primary excitation voltage V_p and the primary winding number of turns N_p are used as three input parameters. GPR is a powerful machine learning method for such low-featured data and provides a Bayesian-based regression capable of measuring uncertainty in predictions. The data generated in the ANSYS/MAXWELL environment for core loss estimation is chosen at random using the parametric FEA setup. The Matern 5/2 kernel function is used to train these data using GPR. Thus, the results are pretty satisfactory; Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) performance metric values are calculated as 0.0102, 0.0029, and 0.0534, respectively. Also, the estimated results are very close to the simulation value. As a result, the GPR method can be used as a reliable tool for estimating core losses with high accuracy during the transformer design stage.