This study introduces a chitosan-based paper microfluidic platform that enhances flow-profile differences among honey types (sugar-fed, acacia, polyfloral, and chestnut). Using honey samples of each type collected from four geographical regions, distinct flow profiles were observed, with succinic acid identified as the key factor enabling honey type classification. Physicochemical characterization based on CLSM, DLS, zeta potential, HPLC, and FE-SEM revealed that succinic acid in honey modulates flow profiles by structurally loosening the chitosan network. A k-Nearest Neighbor model trained on 400 flow profiles achieved an accuracy of 0.92 in distinguishing between natural and sugar-fed honey, and 0.78 in classifying botanical origins using 176 test samples. Furthermore, the study suggests that this platform has the potential to detect honey adulteration using sugar syrup. Therefore, this platform serves as a simple, field-deployable tool for verifying the authenticity of honey and its botanical origin, providing a rapid and cost-effective alternative to conventional methods.