Survival analysis is the name for a collection of statistical techniques used to describe and quantify time to event data. In survival analysis we use the term failure to de_ne the occurrence of the event of interest (even though the event may actually be a success such as recovery from therapy). The term survival time speci_es the length of time taken for failure to occur. Data collected as the duration of survival time are called Survival Data. For instance, in health sciences, it may be the time from when a patient is diagnosed with a speci_c disease until he or she dies, in engineering, it could be the time from initial use of a component until it fails to operate correctly or in education, it could be time until graduation. Many parametric models for analyzing such data are available in the literature. Some of the most popular distributions used for modelling the survival time T are: Exponential distribution, Weibull distribution, Log-logistic distribution and Gamma distribution. However, most of the above distributions are members of the family of Generalized Gamma (GG) distributions. The main objectives of this project are: Investigate a survival analysis approach, based on GG-Model Conduct a comparative study between GG-Model and other existing models us-ing three approaches: Kaplan-Meier (KM) analysis, Semiparametric approach (Cox Model), Parametric modeling. For these approaches we used two data sets which are from 2 di_erent _elds: Education and Health.