The Accelerated Life Model is one of the most commonly used tools in the analysis of survival data which are frequently encountered in medical research and reliability studies. In these types of studies we often deal with complicated data sets for which we cannot observe the complete data set in practical situations due to censoring. Such difficulties are particularly apparent by the fact that there is little work in statistical literature on the Accelerated Life Model for complicated types of censored data sets, such as doubly censored data, interval censored data, and partly interval censored data. In this work, we use the Weighted Empirical Likelihood approach (Ren, 2001) [33] to construct tests, confidence intervals, and goodness-of-fit tests for the Accelerated Life Model in a unified way for various types of censored data. We also provide algorithms for implementation and present relevant simulation results. I began working on this problem with Dr. Jian-Jian Ren. Upon Dr. Ren’s departure from the University of Central Florida I completed this dissertation under the supervision of Dr. Marianna Pensky.