Multi-principal element alloys (MPEAs) present a paradigm shift in materials design and consist of multiple principal elements randomly distributed on a crystal lattice resulting in an enormous phase space. On the one hand this presents opportunities to unravel novel properties whereas on the other it presents a large challenge to survey the phase space, presenting a data-science challenge. We present PREDICT (PRedict properties from Existing Database In Complex alloys Territory), a machine learning framework coupled with electronic structure methods whereby properties in MPEAs could be predicted by learning from the binary alloys database. Specifically, we demonstrate predictions of stiffness constants, Young’s modulus, bulk and shear moduli, and Poisson’s ratio in ternary, quaternary, and quinary MPEAs with a high-level of accuracy. A major benefit of this is that for every new composition discovered, the mechanical properties can be computed using only the existing binary alloy database, bypassing the computationally expensive calculations.