NONE 20260716095936272 wseas/wseas content-registration-form@crossref.org content-registration-form International Journal of Applied Mathematics Computational Science and Systems Engineering 2766-9823 Mohamed Maswadah Department of Mathematics, Faculty of Science Aswan University Aswan, EGYPT In statistical inference, entropy quantifies the uncertainty associated with a random variable. This study introduces a novel numerical iteration technique specifically, a kernel estimation method for estimating both the entropy and the parameters of the inverse Weibull distribution. The proposed kernel method is implemented and its performance is compared with that of the Bayesian approach via Monte Carlo simulations. The simulation results indicate that the kernel method yields more accurate estimates and outperforms the Bayesian approach under the dual generalized progressive hybrid censored data. 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