✅ Quantitative structure-retention relationship (QSRR) modeling is an efficient alternative to predict analyte retention times using molecular descriptors. ✅ This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. ✅ Four machine learning methods partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB) were compared. ✅ The GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. ✅ Solvation energy (SE), HOMO–LUMO energy gap (∆E HOMO–LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. ✅ Our study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models, particularly ensemble methods like GB and RF, in optimizing chromatographic analysis. Article Cross-column density functional theory–based quantitative st...