The current article approaches the Bayesian prediction of moving average processes using three well-known priors: g prior, natural conjugate (NC) prior, and Jeffreys' prior. The main goal of the study is to derive approximate one step-ahead predictive densities for moving average (MA) processes using each of the above-mentioned priors. However, the basic contribution is the derivation of the predictive density based upon the g prior. Investigating the performance of the three one step-ahead predictive densities is performed via comprehensive simulation studies using MA(1) and MA(2) processes for illustration. The simulation results show the equivalence of the performance of the three one step-ahead predictive densities based on the three considered priors in the forecasting process.