We compiled a dataset of over 500 CH-p stacking interactions formed between β-galactose residues and tryptophan, tyrosine, or phenylalanine from the PDB. We conducted first-principles calculations using DFT and SAPT0 benchmarked against the domain-localized pair natural orbital coupled cluster singles doubles with perturbative triples (DLPNO-CCSD(T)) level of theory. We subsequently trained random forest machine learning models to predict interaction energies and identified an energetic landscape that defines these CH-p stacking interactions. We found that they are energetically favorable and therefore contribute significantly to the energy of proteincarbohydrate binding, thereby playing a key role in protein-carbohydrate complexation. The energetic landscape for these interactions demonstrates that they have high orientational flexibility and explains the difference in energetics of CH-p stacking interactions formed by tryptophan, tyrosine, and phenylalanine. This information is essential for understanding protein-carbohydrate binding interactions and the rational design of new therapeutics that target these binding sites.