The goal of this work was to investigate the feasibility of implementing machine learning models for predicting the values of aircraft configuration design variables when provided with time-series of mission-informed performance parameters. Neural network models, along with its associated training data, have been generated and tested for aircraft design space exploration scenarios. The bounds of the data used to train the models was partially informed by the configuration characteristics of the Boeing 737 Next Generation family. The effects of varying neural network architecture, along with the application of different data filtering schemes, on the models’ predictive accuracy have been examined. The results obtained demonstrated that cascade-forward shallow neural network models not only exhibited excellent generalization across the design space for which the model was calibrated for, but also showcased its versatility when tasked with predicting design variable values for a configuration layout relatively different than the ones used for training. Furthermore, the models had favorable metrics in computational wall-clock time required and number of epochs needed for training.