The excited-state properties of molecular crystals can be calculated within the framework of Green's function based many-body perturbation theory. The GW approximation, where G represents the one-particle Green's function and W represents the screened Coulomb interaction, can be used to calculate properties associated with charged excitations, such as the fundamental band gap. Subsequently, the GW quasiparticle energies are fed into the Bethe-Salpeter equation (BSE) to calculate properties associated with neutral excitations, such as the singlet and triplet excitation energies. The high computational cost of GW +BSE is prohibitive for large-scale materials screening. Machine learning (ML) can be used to perform preliminary screening and reduce the need for expensive simulations to a smaller number of promising candidates. However, training ML models typically requires a large amount of data, which is difficult to acquire with GW +BSE. Compared to DFT and even TDDFT datasets, GW +BSE datasets are scarce and contain a relatively small amount of data for isolated molecules or for inorganic crystals with a small number of atoms in the unit cell. This has limited the ability to train ML models to predict the excited state properties of molecular crystals.