Research in two-dimensional atomic crystals has recently focused on their heterostructures, and the advancements in this emerging field has already led to fascinating discoveries such as superconductivity and magnetism. However, thousands of different 2D layered materials and their permutations amount to almost infinite heterostructure combinations. This research will develop a novel ML-guided DFT framework, in conjunction with physically motivated atomistic descriptors, which applies data science in the search for designer heterostructures with targeted properties. As a proof-of-concept, we will demonstrate heterostructures with perfect light absorption through optimizing the band nesting between the filled and empty bands as well as giant piezoelectricity through engineering the electronegativity dipole moments. These heterostructures identified with the targeted properties will be grown with ultra-clean state-of-the-art MBE approaches, and their absorption and piezoelectric coefficients characterized. Corroboration between experiments and theory will then instruct on possible improvements to the proposed ML and DFT models and overall strategy. The successful demonstration of these new designer 2D heterostructures would usher in a new era of efficient and purposeful materials design methodology.