Organic molecules, known as dyes, which can absorb and emit light, are potential candidates for quantum computing owing to their unique properties, including exciton delocalization and coherence features when dyes are aggregated. Importantly, exciton delocalization and coherence can occur at ambient temperature. These novel applications are controlled by dye properties, requiring high extinction coefficient, high transition dipole moment, good aggregation ability, and high exciton exchange energy. Dye aggregate networks via deoxyribonucleic acid (DNA) templating exhibit exciton delocalization, energy transport, and fluorescence emission. DNA nanotechnology provides scaffolding upon which dyes attach in an aqueous environment. To better control the process and optimize the properties, we applied machine learning-driven multiscale modeling techniques to identify candidate dyes and reveal their dye aggregate-DNA interactions and the dye orientations. Those structural features were found to have a strong impact on the resultant performance of the DNA-templated dye aggregates. The computational results were validated with experiments.