The aim of this work is to implement decision tree classifiers (DTCs) capable of distinguishing bee honey by geographical origin. The case study focuses on honeys from the lowland and highland regions of Chiriquí, Panama. Characterization was conducted by analyzing their typical physicochemical and aromatic profiles using AOAC, IHC, and e-Nose methodologies, respectively. Data mining provided insights into the most relevant features, enabling the reduction of an otherwise extensive and resource-intensive dataset. The critical markers identified include reducing sugars, ash, antioxidant capacity, HMF, as well as aromatic, aliphatic, hydrocarbon, and sulfur compounds. This simplified set of features produced an intuitive classification scheme, achieving up to 86% accuracy. This proof-of-concept demonstrates that interpretable models can effectively leverage easily measurable characteristics for regional differentiation, offering a valuable tool for traceability in the Panamanian honey industry.