Nanofluids, engineered colloidal suspensions of nanoparticles in base fluids, demonstrate superior heat-transfer capabilities in industrial and energy systems, outperforming conventional fluids because of enhanced thermal conductivity and convective heat-transfer performance. Their
performance is even better in porous media, mainly because of the larger surface area and the complicated fluid–solid interactions that naturally improve heat transfer. That said, there are still some practical issues that prevent wide use. The main ones are colloidal instability, sedimentation
of nanoparticles, and the relatively high computational cost of simulating these coupled multi-physics systems. This review tries to bring together what has been done recently, especially the combination of hybrid nanofluids, porous structures, and artificial intelligence methods.
A particular point of interest here is the growing use of machine learning models like artificial neural networks and support vector machines for prediction and optimization, and how these are being coupled with CFD models to deal with computational limitations. When the experimental,
theoretical, and numerical results are considered together, a more consistent picture starts to appear. Most studies report a thermal improvement of around 20–30% compared to base fluids, although the exact value changes depending on conditions. At the same time, there are still clear limitations that need to be addressed before real-scale applications become practical. Overall, it seems that the best performance does not come from improving nanofluids or porous media separately, but from using them together. Looking ahead, efficient thermal systems will likely depend on both stable hybrid nanofluids and AI-based design tools. For aerospace applications like unmanned aerial vehicles, the key issues will probably be flowing stability and developing lightweight porous structures that can be manufactured reliably.