Abstract: Background and Objective: The grey model GM(1,1) has been widely applied in various areas. However, the performance of the traditional GM(1,1) model usually indicates certain limitations which may affect the applicability directly or otherwise of the model and its prediction precision. Therefore, the improvement of GM(1,1) model is an important issue. The current study aimed to improve the prediction accuracy of GM(1,1) model. Specifically, in order to improve the prediction accuracy of GM(1,1) model, it is necessary to consider improving the initial condition in the response function of the model. Therefore, the rationale behind this study is to come up with a new approach to improve prediction accuracy of GM(1,1) model through an optimization of the initial condition. Methodology: In this study, the new modified GM(1,1) model is proposed by optimizing the initial condition. The new initial condition consists of the first item and the last item of a sequence generated by applying the first-order accumulative generation operator on the sequence of raw data. Weighted coefficients of the first item and the last item in the combination as the initial condition are derived from a method of minimizing the error summation of the square. Results: In this study, the numerical results show that the modified GM(1,1) model gives a better prediction performance when compared with the traditional GM(1,1) model. Conclusion: The result also shows the efficiency and effectiveness of the new approach.