The oil is an important economic good and its price in international markets is very influential and the ability to correctly predict its price is one of the most important scientific challenges across the world. This article investigates the oil price prediction by using meta-analysis and its comparison with other methods. This article has used AR, ARMA phasal, Tanaka phasal, neural network, simulated data, and other data related to the oil price prediction in Brant stock market from 2004 t0 2013. In nonlinear methods, this article has used influential variables on oil price including oil supply of OECD countries, OPEC oil production, the capacity of oil refineries of OECD, gold price, economical growth of G7, and the price of Brent oil. Fixed and random effects of each study have been specified and have been used for prediction. The results show that meta-analysis is much more accurate in comparison to other linear and nonlinear methods, so it is recommended to use meta-analysis to increase the certainty index of prediction.