Corrosion of steel-reinforced concrete in coastal environments remains a critical durability challenge due to chloride ingress and accelerated steel oxidation. This study presents a fully computational framework that integrates Density Functional Theory (DFT) and Quantitative Structure–Activity Relationship (QSAR) modeling to identify and evaluate eco-friendly corrosion inhibitors derived from biomass. Five natural compounds—vanillin, ferulic acid, cysteine, histidine, and ethyl caproate—were optimized using DFT (B3LYP/def2-TZVP), and quantum descriptors (HOMO, LUMO, dipole moment) were extracted. These descriptors were used to train Multiple Linear Regression (MLR) and Random Forest Regression (RFR) models to predict inhibition efficiency (IE%). The RFR model showed strong performance (R2 = 0.90, RMSE = 10.88), with ferulic acid and vanillin emerging as top candidates (72.9% and 75.6% IE%, respectively). In silico profiling confirmed their environmentally benign nature: Log P < 2.2, Lipinski compliance, low aquatic toxicity, and high biodegradability. DFT-based adsorption energy calculations revealed moderate chemisorption on Fe(110), consistent with HOMO alignment to the iron work function (–4.67 eV). This strategy enables the rational design of scalable green inhibitors for marine-exposed infrastructure. Future improvements should include periodic models and dynamic simulations to enhance accuracy and applicability.