Depending on the nature, objectives, and constraints of the decision variables; linearprogramming, nonlinear programming, integer programming, mixed integer programming etc. can beclassified. Extensive research has been conducted to solve all types of these problems in a parametriccontext. In this paper, to solve optimization problems having uncertainties represented by a singleparameter on the objective function, a systematic linearization approach is developed considering theparametric expression as nonlinear. In the proposed approach, the objective function is considered asnonlinear which is converted into linear by using first order Taylor series expansion at the points makingthe parametric costs zero. Thus, the optimal solution is obtained from the constructed linearprogramming problem. In this way, by determining the intervals in which the optimal solution changes,the solution of the parametric linear programming problem is obtained. A numerical experiment isillustrated to present the effectiveness of the proposed approach.