A small number of residues at protein–protein interfaces, commonly referred to as hotspots, dominate binding free energy and play a decisive role in stabilizing protein complexes. Identifying these residues is central to understanding the energetic architecture of protein–protein interactions and to developing strategies for therapeutic intervention. Although experimental approaches such as alanine scanning have provided critical insights, they are often impractical for large or dynamic systems. This has positioned computational approaches at the forefront of hotspot analysis. This review highlights recent developments in molecular dynamics simulations and machine-learning–based predictors for hotspot identification, discusses current challenges, and outlines emerging directions in the field. Finally, we suggest that combining these complementary approaches could offer a powerful framework for capturing the dynamic and energetic complexity of protein interfaces, making hotspot predictions more robust and interpretable.