Metabolic diseases, including obesity, type 2 diabetes mellitus, and non-alcoholic fatty liver disease (NAFLD), have prompted interest in identifying natural products capable of modulating digestive enzymes. This study comparatively evaluated the phytochemical composition, antioxidant activity, and digestive enzyme inhibitory activities of green tea (GT), black tea (BT), and green coffee (GC) extracts. Phytochemical screening, antioxidant assays (DPPH, ABTS, and SOD-like), and quantitative phenolic analysis indicated that GT contained the highest phenolic content and exhibited the greatest antioxidant activity, whereas GC showed the highest alkaloid content. Among the fractionated extracts, the ethyl acetate fraction of green tea (EtOAc fraction of GT) was selected for further investigation based on its phenolic content, pancreatic lipase inhibitory activity, and overall biological profile. HPLC analysis identified ferulic acid and chlorogenic acid as major constituents, together with syringic acid, resveratrol, myricetin, and rosmarinic acid. Both the crude BT extract and EtOAc fraction of GT exhibited α-amylase inhibitory activity comparable to that of quercetin. Kinetic analysis indicated competitive inhibition by the EtOAc fraction of GT (IC50 = 0.662 mg mL-1), whereas BT displayed mixed-type inhibition with a strong uncompetitive component. The EtOAc fraction of GT also showed the lowest IC50 against pancreatic lipase (0.264 mg mL-1) and followed a non-competitive inhibition mechanism. The EtOAc fraction maintained 98.94% of human skin fibroblast viability after 48 h at 500 µg mL-1 in a preliminary cytotoxicity screen. Molecular docking was consistent with the experimental findings, suggesting that hesperidin and rutin may contribute to competitive α-amylase inhibition and allosteric pancreatic lipase modulation. These findings indicate that the ethyl acetate fraction of green tea is a phenolic-rich fraction with dual digestive enzyme inhibitory activity that merits further evaluation in appropriate biological models.
