The optical band gap is one of the most essential electronic properties of CP materials for their use in photonic and electronic devices, such as organic solar cells (OSCs), organic light-emitting diodes, and organic field-effect transistors. Quantum chemistry simulations, particularly Density Functional Theory (DFT) and Time-Dependent DFT (TDDFT), are indispensable in polymer science for predicting and rationalizing the properties of polymeric materials. These methods offer insights into molecular properties across both ground and excited states and facilitate the prediction of optical gaps. While these simulations can handle sizeable molecular systems efficiently, the correlation between experimental measurements and DFT/TDDFT calculated values often remains weak for several reasons. The optical band gap is the energy required for a photon to excite an electron from the ground state to the first excited state, typically measured using UV-Vis absorption or photoluminescence spectroscopy. These measurements incorporate all physical effects presented in the system, such as solvent effects, vibronic coupling, and other fine details of the electronic structure. In contrast, the DFT-calculated HOMO-LUMO gap measures the energy required to move an electron from the highest occupied molecular orbital (HOMO) to the lowest unoccupied molecular orbital (LUMO), ignoring the Coulombic interactions between the excited electron and the hole, which are involved in the optical band gap. Also, the HOMO-LUMO gap represents a vertical electronic transition and misses the relaxation transition due to interactions of the excited states with the surrounding environment. On the other hand, the TDDFT method calculates excited state energies based on the time-dependent response of the electronic system to an external perturbation. The lowest excited state in TDDFT corresponds to the energy required to promote an electron from the ground state to the first excited state, including excitonic effects and accounting for the dynamic response of the electrons. This provides a more accurate description of excitation energies compared to static DFT. The accuracy of both DFT and TDDFT depends on the choice of exchange-correlation (xc) functional, with hybrid functionals generally yielding better results. However, discrepancies can still arise due to functional approximations and the lack of consideration for experimental conditions. Moreover, higher-level quantum mechanical theories, such as GW method coupled with Bethe-Salpeter equation, which might offer improved accuracy, are impractical for large systems such as CPs due to their computational demands. Data-driven machine learning (ML) approaches are powerful tools for the rapid property predictions and virtual structure screening of organic molecules and CP materials, offering substantial time and cost advantages over traditional experimental and computational methods. The effectiveness of ML models depends crucially on the availability of adequate and reliable training data, as well as the selection of appropriate descriptors that capture the structural and physicochemical properties of CPs. These descriptors include topological, electronic, geometrical, and molecular fragment attributes. Previous studies indicate that different ML algorithms trained with identical descriptors often yield similar accuracy. However, descriptor selection significantly impacts model performance, underscoring its dominant role in determining prediction accuracy. So far, ML methods are increasingly used to predict the electronic properties of CPs and their derivatives, with most studies focusing on small molecules. The unique complexities of CP systems remain less explored, resulting in a scarcity of robust models that accurately reflect the behavior of these polymers. Additionally, many studies utilize DFT-calculated HOMO-LUMO gaps (E DF T gap ) as reference data, which are generally less correlated with experimentally measured optical gaps (E exp gap ) of CPs. Some studies incorporate E exp gap data from small CP datasets, but these models generally show low performance and debatable robustness and transferability. Furthermore, due to the absence of underlying physical principles and the use of obscure descriptors, even well-trained ML models for interpolation struggle with robust performance when extrapolating to new CP design spaces. To the best of our knowledge, there remains a significant gap in the development of well-established ML models that can predict the experimentally measured optical gap of CPs with high accuracy and transferability.