CO2 is a greenhouse gas that traps heat in the atmosphere and is responsible for global warming, rising sea levels, and other environmental hazards. To trap C02 from the air, researchers have developed different solvent-based (chemical/physical) and membrane-based technologies. Electrochemical reduction can also provide carbon sources for higher-value product formation but it cannot use atmospheric CO2 directly so it is not very cost-effective. Chemical solvents involve the formation of covalent bonds between CO2 and solvent but they too pose additional costs in the solvent regeneration step. Physical solvents such as methanol involve physical affinities such as polarity to dissolve a compound. The techniques involving physical solvents and membranes are by far the most efficient methods. We shall discuss how machine learning is employed to make advancements in these technologies. CO2 and N2, two of the major gases in the air, have similar kinetic diameters. So, size sieving using membranes can be a challenging task. A more reasonable approach is to introduce functional groups that selectively interact with CO2 in the polymeric or crystalline membrane. This research aimed at discovering CO2-philic functional groups by comparing the interaction energies of over 133,000 molecules collected from the GDB-9 database. These molecular structures were first reduced from their three-dimensional structures to a machine-readable format called molecular representation. Here, a novel representation, called Persistent Image (PI), is introduced. It uses the number of bonds, bond lengths and the number of rings to generate a unique image for each molecule. These images were generated by the OpenBabel65 soft-ware. To construct a persistent image for a given molecule, spheres centered at each of its atoms are considered. As the radii expand, the neighboring spheres overlap and give rise to homological features known as connected components and holes. The connected components describe interatomic distances while the holes represent functional groups and rings. A PD is then vectorized into a Persistence Image (PI) which is a stable and computationally tractable representation encoding the molecular geometry. A Gaussian kernel is placed at each point on a PD. If all the atoms are treated alike it may lead to inconsistencies. Therefore, to further differentiate between molecules with the same number of atoms and similar bond lengths, variance such as electronegativity difference is introduced. PI offers a similar size vector representation regardless of the system size which makes it a better alternative for other widely used molecular representations such as Coulomb Matrix (CM), Bag of Bands (Bob), and Smooth Overlap of Atomic Positions (SOAP). To verify this claim, three iterations were performed, each employing a different ML model and a different molecular representation to find the most accurate method. Kernel Ridge Regression (ML model) trained on Density Functional Theory (DFT) calculated the interaction energies of these molecules with Co2 using PI. Despite a very small training set (220 molecules), the ML-based screening was able to identify many promising molecules in the GDB-9 database. But only the method that utilized the PI molecular representation was able to identify 4,5-diamino-1H-imidazol-2-ol as the one with the strongest CO2 affinity. It had the highest CO2 interaction energy of 7.41 kcal mol1. Another research used a descriptor-based chemoinformatics model for predicting CO2 solubility in physical solvents in the form of mole fraction. The new descriptor representation included structural and bond information, relevant thermodynamic properties of the solvents (density, vapor density, vapor pressure, dipole moment, boiling point), and experimental operating conditions (temperature, partial pressure of CO2) as the model input variables.