A comparison and validation between the analysis and vibration test data of a nuclear fuel assembly were conducted. During the comparison and validation process, various parameters that govern the vibration behavior of the fuel assembly were determined, including nuclear fuel rod’s stiffness, spring constants of the dimple and spring of support structures, and damping coefficients. The calibration of the vibration analysis model aimed to find analysis parameters that can accurately simulate the vibration behavior of the test data. For calibration, power spectral density (PSD) diagrams were generated for both the measured signals from the test and the calculated signals from the analysis. The correlation coefficient between these two PSD plots was calculated. To find the analysis parameters, each parameter was defined as a variable with an appropriate range. Latin hypercube sampling was used to generate multiple sample points in the variable space. Analysis was performed for the generated sample points, and PSD plot correlation coefficients were calculated. Using the generated sample points and their corresponding results, a Gaussian Process Regression model was implemented for PSD plot correlation coefficients and the maximum PSD value. Based on the constructed surrogate model, the optimal analysis parameters were easily found without additional computations. Through this method, it was confirmed that the analysis model using the optimal parametes appropriately simulates the vibration behavior of the test.