The transition from traditional batch to continuous pharmaceutical manufacturing puts additional demands on the efficient process development and operation. The comprehensive understanding of complex interdependencies between critical process parameters (CPPs) and critical material attributes (CMAs) for the plants consisting of several unit operations is very challenging for process operators and experts. Therefore, the development of computational models is necessary to implement active process control and ensure a control state. Here, we present a machine-learning (ML) based approach to build a data-driven process model and to implement real-time process control for a continuous wet granulation line. The analysis of historical process data, where a set of experiments was performed for a targeted collection of new data, has allowed us to successfully build an ML kernel and to implement a control system for the granulation plant. Furthermore, to support the ML training process, the process data was extended with mechanistic models implemented as soft-sensors, resulting in a hybrid model architecture. The performed tests have shown that the proposed strategy and the developed ML system can be efficiently used to perform real-time control of the continuous plant and to achieve desired CMAs such as size and loss on drying of the final granules by adjusting CPPs.