Τhe objective of this PhD dissertation is the development of MPC (Model Predictive Control) and EMPC (Economic Model Predictive Control) algorithms for non-linear systems using computational intelligence and machine learning models for system modelling. Emphasis will be given to RBFNs (Radial Basis Function Networks) for predicting the behavior of these dynamical systems. Comparative analysis of the ML models in terms of accuracy and computational time for system modelling will be conducted by testing them to a number of open-source datasets from various scientific fields, in order to capture the performance of the models that prevail. From the perspective of automatic control, the development of MPC schemes will be realized by integrating the ML models, as predictive models utilized by the controller. Additional study will be implemented for tuning the controllers regarding the suitable operating points, while also considering stability and robustness issues. Finally, MPC controllers in conjunction with the developed ML models, will find real-world application in a waste treatment plant.