An ML-driven automation for the optimization process for 3D printed biopolymer constructs, my work applies hierarchical machine learning to predict the generalized process settings and material composition for optimal print performance from a small dataset of just 48 prints. I developed process maps for the first time for bio-polymers with non-Newtonian fluidic properties which map machine settings and subsequent predicted error onto a graphical space to provide decision support for the designer.