Numerical simulations, based on relatively complex physical models developed for CFD, can accurately predict engine-out responses, but they require huge memory space and/or computation time. In terms of resources and computer time, artificial intelligence methodologies are more cost-effective. In this work, we used an ANN to predict the performance and exhaust emissions of a single-cylinder Diesel engine running on fossil diesel, biodiesel, and their blends under various speed and load regimes. To perform the modeling, we employed multilayer perceptrons and a back-propagation gradient algorithm with momentum to train the network weights. The modification of the network weights was done using the second-order method of Levenberg-Marquardt, and the technique of early termination was utilized to avoid overtraining the model. The study involved using 70% of the complete experimental data to train the neural network, allocating 15% for network validation, and reserving the remaining 15% to evaluate the trained network effectiveness. The ANN model that was created demonstrated remarkable accuracy in predicting both engine performance and emissions. This is evident from the strong correlation coefficients observed, which ranged from 0.987 to 0.999, as well as the low mean squared errors ranging from 7.44?10-4 to 2.49?10-3.