A comparison was made between the method of maximum Likelihood and the method of Bayes in estimating the parameters of the (GEV) (Generalized Extreme Value distribution) , by using Newton Raphson to find nonlinear equations which producer from the estimation and the results showed that the estimators of the non-informative Bayes method are better in estimation than the method of maximum Likelihood (ML), where the Bayes estimators (MSE) are smaller than the estimators (MLE) with sample sizes (25,50,100) , and at the sample size (500) the estimators were similar to the assumption values of the parameters, real data were used on the applied side and some statistical indicators were given that showed that random temperature data suffer from extremism only and do not suffer from anomalies, skewness or , kurtosis which makes the distribution of the (GEVD) suitable for representing temperature data.