Gear is the most essential element in power transmission system. Helical transmission gearrncan operate at high speed with large load carrying capacity. Due to this high contact stress isrncreated at the mating surfaces. One of the main gear tooth failure type is contact fatigue failurerndue repetition of high contact stresses. In addition to design aspects, two important areas needrnto be addressed in order to enhance helical gear damage due to contact fatigue; improvementsrnof material and enhancement in heat treatment. But it is very difficult to develop a completerntheoretical/analytical model to improve the material property and heat treatment. In addition,rnto perform those enhancements, it needs an experimental work. In this study prediction ofrnmechanical property of helical gear material using artificial neural network (ANN) andrnanalyzing the contact fatigue of the predicted materials has been performed. After trainingrnthe network, different performance measurements of the neural network accuracy was takenrnand prediction of the new concept (mechanical property) was performed. From five candidaternmaterials, concept one was selected. By using the developed mechanical properties, contactrnfatigue was analyzed using AGMA standard and Finite Element Method. The resultsrnindicates, the fatigue life is infinite until the contact stress reach 959.7 Mpa. But beyond thisrncontact stress, the fatigue life is limited and decreased. The comparison of contact stress byrnusing AGMA and Ansys for the predicted material using ANN has shown and an error ofrn4.46 % and below was obtained. The material has best performance until the appliedrntangential load reaches 2000 N, because for applied tangential load of 2000 N, the factor ofrnsafety for AGMA as well as Ansys is greater than one. This indicates that, it is selectiverntechnique to predict the mechanical properties of materials using ANN model, when there isrnlimited condition to use experimental investigation, because ANN simulates any correlationsrnthat are difficult to describe using physics based models.