We demonstrate the application of a Recurrent Neural Network to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two Long-Short-Term Memory (LSTM) frameworks in this study which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physico-chemical properties, mixer geometry, and operating conditions. Our results demonstrate that whilst it is possible to train a LSTM with a single fully-connected layer more efficiently than a LSTM Encoderdecoder, the latter is shown to be more capable of learning the dynamics underlying dispersion metrics. Details of the methodology are presented, which include data pre-processing, LSTM model exploration, methods for model performance visualisation; an ensemble-based procedure is also introduced to provide a measure of the model uncertainty. The workflow is designed to be generic and can be deployed to make predictions in other industrial applications with similar time-series data.