This dataset contains scripts and data supporting the research article, "Multi-Fidelity deep learning for predicting the nonlinear buckling behaviour of concrete thin-shells". Concrete thin-shells are materially efficient structures, which can be used to reduce the environmental impact of concrete structures. Their shape is typically determined iteratively and evaluated through Finite Element Analysis (FEA). This research proposes the use of surrogate models as faster alternatives to FEA, thus enabling wider design space exploration. This dataset contains deep learning models – Multi-Fidelity Multilayer Perceptrons – that have been trained to predict the nonlinear buckling factor of concrete thin-shells of various shapes under design loads. It also contains the Python scripts that were used to train these models and assess their performance. Running these scripts necessitates the associated ConcreteShellFEA dataset to be downloaded. Further details about this data can be found in the related research article.