In this context, data-driven models have recently emerged as a promising approach for quantifying the structure-property relationships in two-phase composites. Specifically, machine learning approaches have been employed to develop surrogate models for quantitative prediction of macroscopic properties with binary composite microstructure as input, represented by either low-dimensional structural descriptors or as representative volume ele-ments. In the studies pertaining to the former, the hand-crafted features representing the microstructure are linked to an associated property using a regression-based model or artificial neural network. For the latter case, convolutional neural networks (CNNs) are employed to extract important features from the digitized images of composite microstructures, generated by stochastic growth of grains or by random assignment of numbers in the uniformly voxelized grids, in order to predict the effective property of interest. Motivated by the above advances, we hypothesize that machine learning techniques can be similarly applied to correlate the properties of nanocomposites reinforced with spherical nanoparticles to their microstructure. Further, integrating such structure-property linkages with microstructure optimization algorithms could significantly accelerate the exploration of the relevant high-dimensional configuration spaces to identify nanoparticle morphologies possessing optimal characteristics.