We introduce a new bayesian regression model called the bayesian generalized partial linear model which extends the generalized partial linear model pioneered by Severini and Staniswalis (1994).This paper considers bayesian estimation and inference of parameters for the generalized partial linear model (GPLM) using some multivariate conjugate prior distributions under the square error loss function, We introduce a new algorithm for estimating the (GPLM) parameters by using bayesian theorem in more detail, Finally, Comparisons are made between (GPLM) estimators under using Bayesian Technique and without using it using simulation study.