In this study, we look at some estimation issues for complementary unit Weibull distributions in the context of adaptive progressive type-II hybrid censoring. The point and interval estimations of the model parameters, as well as a number of its reliability indices, are explored. The likelihood frequentist approach is used as a classical strategy to obtain the point and approximate confidence ranges. The median parameter of the distribution is produced in a closed form as a function of the shape parameter, while the shape parameter can be obtained iteratively. The squared error loss function and gamma and beta prior distributions are used for evaluating Bayes estimates. The Markov chain Monte Carlo method is used to solve the difficult posterior distribution expression in order to provide Bayes estimates and the highest posterior density credible ranges. A simulation study is done to evaluate the efficacy of various estimating methodologies making use of different circumstances for sample sizes and progressive censoring strategies. Finally, three real-world datasets from veterinary, industrial, and physical applications are examined to highlight the practical importance of the provided methodologies.