<p>Bayesian estimation of parameters may deal with imprecise information instead of exact information. Hence, we need to extend the usual Bayesian approach. This paper investigates the estimation of the parameter estimation of Gompertz distribution using Bayesian, expected Bayesian and hierarchical Bayesian estimation techniques based on type-II censoring fuzzy data. To this end, we use the gamma distribution as a prior distribution and obtain these estimators of parameter based on fuzzy data under two different loss functions: the Entropy loss function and the linear exponential loss function. In order to compare Bayesian, expected Bayesian and hierarchical Bayesian estimation methods, these estimation techniques are performed using Monte Carlo simulations. Finally, the efficiency of the proposed methods was demonstrated using a set of real data from the engineering field to clarify the applicability of the suggested estimators to real phenomena.</p>