Cancer currently ranks as the second most common cause of mortality worldwide, primarily due to uncontrolled cell growth driven by aberrant mitotic processes. Aurora A kinase (AURKA), a key regulator of mitosis involved in centrosome maturation, bipolar spindle formation, and cytokinesis, has been identified as a promising anticancer target. This study employs a comprehensive computational approach to identify new AURKA inhibitors. Using MOE software, a ligand-based pharmacophore model was developed based on six potent AURKA inhibitors. The model, consisting of three features-Aro/HydA, Acc, and Don/Acc-at an 80 % threshold, demonstrated strong discriminative power with a sensitivity of 69.8 %, specificity of 63.6 %, and accuracy of 60.4 %. Screening of the ZINC database yielded 774 hits, from which A1 (ZINC63106872) and A2 (ZINC39272872) were identified as the top candidates, with superior docking scores (-9.24 and -8.97 kcal/mol) compared to the reference MK-5108 (-7.49 kcal/mol). These hits satisfied Lipinski's rule and exhibited favourable ADMET profiles. DFT analysis revealed higher dipole moments (A1: 6.15 D, A2:6.39 D) and narrower HOMO-LUMO gaps (A1: 0.33 eV, A2: 0.38 eV), indicating enhanced polarity and reactivity. MEP plots showed defined donor-acceptor zones for both compounds, having a balanced surface. Molecular dynamics simulations over 500 ns confirmed complex stability, with protein backbone RMSD around 2.8 Å and ligand RMSD of 4.0 Å (A1) and 6.0 Å (A2). RMSF values remained below 2.4 Å. The most favourable binding energy for A1 (-75.34 kcal/mol) in MM-GBSA analysis confirms its strong interaction and therapeutic potential.