In the industrial sector, foaming remains an inevitable side effect of mixing, shearing, powder incorporation, and the metabolic activities of microorganisms in a bioprocess. Excessive foaming can interfere with the mixing of reactants and lead to problems such as decreased effective reactor volume, microbial contamination, product loss, and increased reaction time. Physical modeling of foaming is an arduous process as it requires estimation of foam height, which is dynamic in nature and varies for different processes. This work demonstrates a novel application of ensemble-based machine learning methods for prediction of different fermenter types in a fermentation process (to allow for successful data integration) and of the onset of foaming. Ensemble-based methods are robust nonlinear modeling techniques that aggregate a set of learners to obtain better predictive performance than a single learner. We apply two ensemble frameworks, extreme gradient boosting (XGBoost) and random forest (RF), to build classification and regression models. We use real plant data for 64 batches from four fermenters with different material, geometry, and equipment specifications. Our first task is to develop an ensemble-based fermenter classification model that uses well-known fermentation independent variables for each batch alone, without having to incorporate explicitly the design specifications. The resulting fermenter classification model is able to differentiate or classify the fermenter type with an accuracy of 99.49% for our integrated data sets of over 183 000 instances. This enables us to integrate multiple plant data sets from different fermenter specifications and develop a generalized foaming prediction model. Next, we build classification and regression models for foaming prediction. The resulting models are able to predict the foaming indicator (the exhaust differential pressure) to achieve an accuracy of 82.39% and an RMSE value of +/- 12 mbarg, which is well within the tolerance for foaming prediction in industrial practice. These results demonstrate the effectiveness of ensemble-based machine learning models for fermenter classification, data integration, and foaming prediction involving multiple fermenter design specifications. Using these tools, we can orchestrate the addition of chemical antifoam agents (AFA) or defoamers in an ad hoc manner to mitigate the adverse effects of excessive AFA addition. Our work differentiates itself from previous work in this area through the following contributions: (1) accurate ensemble-based classification modeling to differentiate fermenter types on the basis of known independent variables alone, without prior knowledge of fermenter design specifications, thus allowing for data integration of multiple plant data sets to build better prediction models; (2) accurate prediction of foaming based on exhaust differential pressure using both classification and regression models; and (3) usage of a large, industrial, multivariate fermenter data set.