Researchers are exploring the intersection of machine learning and complex networks, with a focus on embedding these networks in hyperbolic space. Various approaches are being developed, including coalescent embedding, iterative embedding and reweighting, and hyperbolic matrix factorization, with applications in fields such as protein interactomes, drug-target associations, and brain network analysis. The outcomes of these efforts include improved link reliability, prediction of drug-target associations, and potential applications in fields like epilepsy surgery and maritime network science.