Although these studies show the efficacy of data-driven methods in optimizing 3Dprinting process parameters and predicting the properties of polymer-based materials, they often ignore material properties in the prediction algorithms and their optimization processes tailored for specific types of materials. To the best authors' knowledge, there is currently no research on PNCs that encompasses both material properties and process parameters to forecast their printing and final properties. However, in the field of food science, Ma et al. conducted a noteworthy study in which they developed a predictive model to estimate the extrudability and geometry of food materials using the rheological properties of the materials and printing parameters as input parameters. By these means, they achieved relatively good machine learning predictions regarding the quality of the printed samples. The work effectively demonstrates the potential of machine learning models to capture the complex relationships specifically between material properties and their printability.