<p>This paper develops a comprehensive statistical framework for analyzing a progressive-stress accelerated life test model under progressive censoring, when transformed latent failure times follow a unit inverse Weibull distribution. The progressive stress is assumed to be proportional to time, and a cumulative exposure model is adopted to account for the effect of changing stress levels. We develop the maximum product of spacing estimators as an alternative to frequently used maximum likelihood estimators. Appropriate prior specifications are implemented to derive Bayes estimates under different loss functions. Interval estimation is also considered, and bootstrap, asymptotic, and credible intervals for the parameters are constructed. We have conducted a Monte Carlo simulation study to evaluate the efficiency and precision of the proposed estimators. Finally, the model’s practical applicability is demonstrated using a real-world data set, highlighting its effectiveness in reliability analysis under progressively changing stress conditions. Optimal plans are discussed by implementing various optimality criteria under different schemes.</p>