Metal parts manufactured via Powder Bed Fusion (PBF) process show great potential in industrial applications. Hierarchical, heterogeneous microstructure characteristics of the PBF-built alloys pose a significant challenge to the prediction of structural performances. To enable computational engineering of this type of materials, multiscale microstructure modeling framework has been proposed to predict the stochastic material properties. AlSi10Mg built by Selective Laser Melting (SLM) is selected as the demonstrative example. At the microscale, the epitaxial granular structures are reconstructed based on Scanning Electron Microscopic Electron Backscatter Diffraction (SEM EBSD) images. The microscale analysis provides property inputs for the mesoscale model, which captures the fish scale like melt pools at the millimeter scale. The predicted material properties are compared with the experimental data for further calibration of the material constitutive models. One critical challenge is that some parameters in material models cannot be directly obtained from experimental tests. In this work, we establish a machine learning-based model calibration framework to predict the unknown material parameters. Furthermore, several machine learning methods are compared to shed lights on their capability of capturing the relation between input parameter values and the resultant prediction errors.