The challenging explored subject under non-Bayesian and Bayesian techniques is estimating parameters of Gompertz distribution based on scheme of progressive Type-I censoring. Therefore, Maximum likelihood estimators for the unknown parameters, as well as asymptotic confidence intervals, are determined. Bayes estimates with the estimates of the associated greatest posterior density credible interval are derived using squared error loss function. Using the Metropolis-Hasting algorithm and the method of Markov Chain Monte Carlo (MCMC), estimates of Bayes are summarized. To assess the proposed estimator's performance, a Monte Carlo simulation study is accomplished. Furthermore, the theoretical conclusions of Bayes estimates and maximum likelihood estimates at progressively Type-I censored samples specified schemes are illustrated using an examined analysis on real given data.