Diabetic Neuropathy (DN) is a severe diabetes complication linked to progressive peripheral nerve degeneration, oxidative stress, mitochondrial dysfunction and chronic neuropathic pain with no effective disease-modifying therapies available to date. Due to the key role of MAO-A in monoamine metabolism and reactive oxygen species production, its inhibition may help reduce oxidative stress–related neuronal damage in diabetic neuropathy. This study used a computational drug discovery approach targeting MAO-A. Ligand-based pharmacophore modeling was performed using 52 caffeine derivatives from the literature to create a dataset for energetic evaluation and identify new MAO-A inhibitors. A five-feature AAHRR hypothesis comprising two hydrogen bond acceptors, one hydrophobic and two aromatic rings was generated using the PHASE 3.0 module. A series of these 1,000 drug-like compounds were then retrieved by screening the ZINC database with validated pharmacophore model. They underwent molecular docking and high-throughput virtual screening against the human MAO-A crystal structure (PDB ID: 2Z5X). Among 591 compounds with appropriate binding profiles, the top ranked compound based on Glide G score was ZINC02123682 [(1r,4r)-4-((2-(5,7-dimethoxy-4-methyl-2-oxo-2H-chromen–3–yl)acetamido)methyl)cyclohexanecarboxylic acid] (Glide G-score = −7.54). The stability of the protein–ligand complex was additionally validated by 1000-ps molecular dynamics simulations in explicit TIP3P water environment using the OPLS_2005 force field, with stable RMSD fluctuations maintained within 2-4 Å and a favourable negative potential energy profile through the period of simulation. These data characterize ZINC02123682 as a structurally optimized and dynamically stable MAO-A inhibitor, providing an attractive lead compound in the development of potential new neuroprotective therapeutics for diabetic neuropathy.