High-fidelity optimisation studies are a useful asset in the design of critical components for large gas turbines. These studies require the computation of numerous computationally expensive CFD simulations and result in predominantly optimisation graphs of design objectives (e.g. Pareto-front figure). The quantity of generated data is intractable and therefore in practice not stored. All the physical insight into the flow-fields is lost and no data can be accessed nor any adjustments can be made. This work researches the combination of CFD simulations and Machine Learning (ML) algorithms to simultaneously: 1) reduce computational cost and time, 2) retain physical insight by focusing on the prediction of flow-fields and 3) keep the ability to access information or to make adjustments. This research starts with the identification of 19 potential holding ML-algorithms. A suitable algorithm is chosen with the help of 4 criteria, that is referred to as the Proper-Orthogonal Decomposition (POD) - Interpolation algorithm. In studied literature it reportedly provided very high speed-up times, good accuracy, additional learning benefit and is relative simple. The POD-Interpolation algorithm is implemented on two test cases: the Von Karman Vortex Street and a Shear Mixing Layer. The results are summarised for both as unphysical and highly inaccurate. Two root-causes are identified: the first is an invalid assumption in the methodology and the second dominant root-cause is a fundamental flaw. Interpolation can never accurately predict dominant frequencies in a flow-field. With the inability of the POD-Interpolation algorithm to predict physical accurate flow-fields, a new proposed algorithm is implemented. This is referred to as the Sparse-Dynamic-Mode-Decomposition (sDMD) - Scaling method. This method relies on accurate scaling relations to predict flow-fields. However, almost no application cases have known scaling relations. The sDMD-Scaling algorithm is capable of discovering and using these newly discovered scaling relations to predict flow-fields. This is proven for the Von Karman Vortex Street case with self-made scaling relations: the Mean Residual Error of the predicted flow-fields rarely exceeds 7%. An error reduction factor of ≈ 10 is achieved when compared to the POD-Interpolation approach. The design space from the test case is computed ≈ 5.6 times faster with the CFD and sDMD-Scaling combination than with the conventional CFD approach. Furthermore, any new design point can be computed ≈ 309 times faster with the trained sDMD-Scaling algorithm than with a CFD simulation. The sDMD-Scaling algorithm proves that it is possible to combine CFD and ML to achieve the 3 aforementioned goals for a canonical fluid dynamic test case. Several recommendations are presented to improve the sDMD-Scaling algorithm for more complex cases. However, given the difficulties and efforts that went into the relative straightforward test cases, it is difficult to foresee any large-scale implementation of any CFD and ML combination for predicting flow-fields.