<p>In this article, we develop the estimation and prediction methods for the power Lindley distribution based on the unified hybrid censored data. The maximum likelihood, maximum product of spacing, and the Bayesian estimation methods are used to obtain the estimates of the model parameters, reliability, and hazard rate functions. The Bayes estimators under symmetric and asymmetric loss functions are obtained using the Metropolis-Hastings algorithm. We derive the asymptotic confidence intervals, Bayesian credible intervals, and highest posterior density credible intervals for all the unknown parameters of interest. Further, the censored and future observations are predicted under the Bayesian framework. A Monte Carlo simulation technique is used to reinforce the theoretical findings. Also, two real-life data sets are studied to illustrate the discussed methods of estimation and prediction.</p>