This study aims to maximize the performance of projects and reduce the cost by using the hybrid model of value engineering and artificial intelligence. The proposed model has been applied on the Trucks (vehicles) industry in Libya. The model aims to achieve optimization on two levels, the first level is the application of value engineering to determine the optimal alternatives available to different parts of the vehicle between the local manufacture of full import or half of a local manufacturing. . . etc. The second level is to train the neural network that has been formulated by the researchers on the available collected data through questionnaires and collected opinions of the client in his favorite specifications and their relation to several factors, including the customs, traditions and economic level. . . etc. The neural network training aims to predict better situations in the future, adopted according to the light change of the factors which affect customer decisions. It was found that the application of this methodology on a sample of customers that they prefer product functions, respectively (safetycomfort and luxury- luxuries level- the shape and interior and exterior decoration- durability- the power of the engine- the allowable weight of the load- the speed of the truck – and the engine age) .