Porous metallic structures play a critical role in mass and heat transfer processes due to their high surface areas, fixed porosity, and high stiffness – so understanding their fluid flow behaviour is crucial in designing materials that perform efficiently in mass and heat transfer. In view of this, a multi-disciplinary approach is employed to study the hydrodynamics of aluminium foams produced by a liquid melt infiltration technique using experimental, computational fluid dynamics (CFD) modelling and simulation, as well as artificial neural network (ANN) machine learning backpropagation. X-ray computed tomography datasets were used to characterize pore-structure-related properties of replicated materials, followed by three-dimensional advanced imaging of workable representative volume elements. Hydraulic flow information was acquired for the porous matrices using the constant-head permeameter technique. Experiments showed the permeability and Forchheimer coefficient dependence on pore-structure-related properties for fluid-flowing within the pre-Forchheimer and fully developed Forchheimer regimes. Flow permeability of 8.479 × 10−09m2 was highest in the material with the widest mean pore openings (0.212 mm) and lowest (1.291 × 10−09m2) in the material with the narrowest mean pore openings (0.106 mm). Conversely, Forchheimer coefficients were higher for materials with lower porosities and lower for materials with higher porosities. CFD calculations accurately predicted the fluid properties of metallic foams, as well as the influence of intrinsic foam properties on permeability and the Forchheimer coefficient. The ANN model framework was also able to provide valuable information about the hydrodynamics of these materials. Convolution and non-linearity of the ANN model were improved by adding supplementary neurons to the hidden layers allowing deviations within 0.3 and 9.0 percent to be attained.