Optimality analysis of linear programming is used extensively in economy and management. The optimal objective value is a complicated piecewise linear function of the right-hand-side vector of the constraints, and its analytical expression is normally hard to obtain. Four metamodels, that is, polynomial regression, radial basis function, Kriging interpolation, and polynomial regression hybridized with Kriging interpolation, are used to rapidly predict the optimal objective function value. Comparative analysis through simulation experiments shows that the last three methods can provide higher accuracy fitting with fewer experimental designs. In particular, polynomial regression method hibridized with Kriging interpolation can not only have a good fitting accuracy but also give a simple approximate expression of the optimal objective function value using a second-order polynomial. The results show that the metamodel method is effective for optimality analysis of linear programming.