In parameter estimation techniques, the Bayes’ method is the most usable technique for estimating distribution parameters, despite being subjective to prior information. Thus, the main objective of this paper is to present an optimal technique using the Runge-Kutta method to find the point estimators for Burr -XII model parameters, based on interval-censored data, and compare them with the Bayesian estimates based on the informative and kernel priors, via Monte Carlo simulations. The simulation results indicated that the Runge-Kutta method is highly favorable, which provides better estimates and outperforms the Bayesian estimates using different loss functions. Finally, real data analyses are presented to demonstrate the efficiency of the proposed methods.