The discovery of effective and environmentally friendly organic corrosion inhibitors remains constrained by slow experimental screening and fragmented computational workflows . This study presents an integrated hybrid framework combining density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML) for high-throughput screening of organic corrosion inhibitors. DFT provides quantum-level insights into electronic structure and adsorption energetics through frontier molecular orbital analysis (EHOMO, ELUMO, energy gap ΔE), while MD captures time-dependent interfacial behavior and competitive ion interactions . A comprehensive dataset of 284 phenyl phthalimide derivatives was generated through DFT and MD simulations, with electronic properties correlated to experimental inhibition efficiency values . Among various ML models evaluated, Artificial Neural Networks demonstrated the highest prediction accuracy, achieving R² values of 93.18% for EHOMO and 91.12% for ELUMO . SHAP and PFI feature importance analyses revealed that descriptors B06[C-N] and qnmax are essential for inhibitor efficacy . The integrated framework addresses key limitations in current approaches including data scarcity, non-standardized descriptor selection, insufficient physical interpretability, and poor generalization across chemically diverse systems . Experimental validation through electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization confirmed the predictive capability of the ML models, with excellent agreement between predicted and measured inhibition efficiencies. This work establishes a unified, scalable, and physically informed computational framework for rational design and discovery of next-generation corrosion inhibitors.