Temporal networks appear in applications ranging from symptom prediction to social networks to epidemiology. This thesis proposes a flexible framework for modelling temporal network progression which utilises a combination of singular value decomposition, scientific machine learning, and symbolic regression. We demonstrate the usefulness of the framework for modelling the progression of a variety of small temporal networks. Additionally, we present a Julia programming package for streamlining work with temporal networks. This thesis demonstrates our framework as a proof of concept, and we are confident that with further research, this framework will be a useful tool for modelling temporal networks in many fields.