Numerical simulation is a useful and a cost-effective tool for engine cycle prediction. In the present study, a dual Wiebe function is used to approximate the heat release rate in a DI, naturally aspirated diesel engine fuelled with eucalyptus biodiesel/diesel fuel blends and operated at various engine loads. This correlation is fitted to the experimental heat release rate at various operating conditions (fuel nature and engine load) using a least squares regression to find the unknown parameters. The main objective of this study is to propose a model to predict the Wiebe function parameters for more general operating conditions, not only those experimentally tested. For this purpose, an artificial neural network (ANN) is developed on the basis of the experimental data. Engine load and eucalyptus biodiesel/diesel fuel blend are the input layer, while the six parameters of the dual Wiebe function are the output layer. Levenberge-Marquardt (LM) learning algorithm is found to be the best learning algorithm with a minimum number of neurons in the hidden layer. The best results obtained by 2-12-6 network architecture show a good performance with a root mean square error (RMS) less than 0.009578, an absolute fraction variance (R2) in the range of 0.99825-0.99999 and a mean absolute percentage error (MAPE) in the range of 0.16-5.61%. Hence, the developed ANN model can effectively be used as a preferment prediction tool for the engine heat release rate.