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The quality and performance of composite-based materials are functions of their mechanical properties. Hence, a scientific basis is needed for the determination of the feasible combination of process parameters that will bring about excellent mechanical properties. This study examines the potential of artificial neural network (ANN) for the prediction of mechanical properties, namely density and hardness of graphene nanoplatelet (GNP)/polylactic acid (PLA) nanocomposite developed under various operating conditions of spark plasma sintering (SPS) technique. A back-propagation having a 2-12-2 architecture and Levenberg-Marquardt algorithm was developed to predict the mechanical performance in terms of density and hardness property of GNP/PLA nanocomposites. The predictions of the modelled results were compared with those of the experimental value obtained. The model gave a low rootmean-squared error and performed well with the correlation coefficient (R) for both outputs; density (0.95497) and hardness (0.9832) found to be close to 1. The results of the predicted data were discovered to be very consistent with the values obtained

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