This study explores the use of Simplified Molecular Input Line Entry System (SMILES) as a sole feature to predict corrosion inhibition efficiency (CIE) for N-heterocyclic compounds, replacing quantum chemical properties (QCP). The gradient boosting regressor (GBR) model outperforms other models such as k-nearest neighbors (KNN) and support vector regression (SVR). SMILES accurately predicts CIE for various datasets, showing a moderate correlation with corrosion inhibition properties. The proposed method identifies novel N-heterocyclic derivatives with high CIE, suggesting its utility in discovering corrosion inhibitors. The study aims to improve the precision of CIE predictions and demonstrate the broad utility of SMILES in accelerating the design and discovery of corrosion inhibitors, offering a promising path for efficient and sustainable exploration of anti-corrosion materials.This study explores the use of Simplified Molecular Input Line Entry System (SMILES) as a sole feature to predict corrosion inhibition efficiency (CIE) for N-heterocyclic compounds, replacing quantum chemical properties (QCP). The gradient boosting regressor (GBR) model outperforms other models such as k-nearest neighbors (KNN) and support vector regression (SVR). SMILES accurately predicts CIE for various datasets, showing a moderate correlation with corrosion inhibition properties. The proposed method identifies novel N-heterocyclic derivatives with high CIE, suggesting its utility in discovering corrosion inhibitors. The study aims to improve the precision of CIE predictions and demonstrate the broad utility of SMILES in accelerating the design and discovery of corrosion inhibitors, offering a promising path for efficient and sustainable exploration of anti-corrosion materials.