This study considers use of Bayesian methodology for the fertility projection of an India using two different way of projecting fertility rate under Bayesian hierarchical model by using Markov Chain Monte Carlo (MCMC) technique. Many ways have been proposed for projecting future fertility but little interest is show in projecting age specific fertility rates in modern population. This study used Age Specific Fertility Rate data for the Sample Registration System during 1971-2012 for India. In this work, two versions of parametric models are proposed in order to describe the typical old way as well as the new one. Model-1 describe the projecting total fertility rates then converted into age specific fertility rates while Model-2 is the improve version are useful for probabilistic projection of age specific fertility. In order to evaluate the adequacy of the model proposed, we fit the observed and estimated data sets of several years. Furthermore, we compare these with other model already existed and use in the current fertility projection method. We have identified some limitation of typical methods and we have proposed several improvements to overcome them. Finally in order to avoid error in projection in fertility rates; model 2 provides the best fits and it superior among other way of projecting fertility because of it model all seven age group of fertility separately. It should be noted that the new method for projecting fertility take account of uncertainty about the overall level of fertility as measured by the age specific fertility rates or total fertility rates. Conditional on fertility measure, however, the projected vital rates are deterministic. There is thus a missing component of uncertainty, and it would be desirable to extend the methods used to take account this, particularly of uncertainty about the future level of fertility.