This study aims to develop a predictive model to forecast the printability and printing quality of thermoplastics and their nanocomposites by utilizing material properties as input. The model uses a variety of material attributes as input, including flow behavior, viscoelastic properties, and thermal properties, as well as printing parameters, to explore how the rheological and thermal properties of the material, along with printing factors, affect the final properties. To do so, a total of twelve binary nanocomposites were produced using two different bio-based PLAs, a commercial-grade nanoclay (NC), and graphene nanoplatelets (GNP) with a twin screw extruder (TSE). The thermal and rheological properties of the as-extruded materials, and pristine PLAs were investigated using Differential Scanning Calorimetry (DSC) and rheological (steady-state and oscillatory) analysis. The results of these analyses were used as input in the predictive model.