In this study, we introduced a model that combines DFT calculations with a ML approach to accurately predict the experimentally measured optical band gaps of CPs, utilizing a dataset of 1096 data points. We first proposed a two-step modification procedure for constructing oligomers to effectively capture π-electron delocalization in CPs: alkyl side chain truncation and conjugated backbone extension. This approach significantly improves the correlation between the DFT-calculated HOMO-LUMO gaps and experimental gaps (R 2 =0.51) compared to the unmodified side-chain-containing monomers (R 2 =0.15). Subsequently, we incorporated both conjugated backbone characteristics, derived from quantum chemistry, and the alkyl-side-chain effects, represented by molecular de- Table : The performance metrics of XGBoost-1 and XGBoost-2 in predicting the experimental optical gaps of conjugated polymers categorized by various acceptor units. Group 1 acceptor units are included in the training set, whereas Group 2 units are not. Chemical structures are illustrated in Figure . XGBoost-1 is trained using ECFP6 alone, while XGBoost-2 is trained with both E oligomer gap and ECFP6. Both root mean square error (RMSE) and mean absolute error (MAE) are measured in eV.