The study aims to predict the printability and printing quality of the thermoplastics and their nanocomposites while innovatively using the material properties as input for the predictive model. Table summarizes the input parameters used for the prediction model. The flow index (n) and flow consistency index (K), determined by steady-state rheological analysis, were utilized as flow indicator inputs. The initial complex viscosity (η * ), initial storage modulus (G ′ ), and average loss factor (av. tan δ) determined by time sweep experiments were utilized as viscoelastic property inputs. During the initial trialand-error-experiments, it was noticed that some materials undergo thermal degradation, which affects the printing process and final quality. As a result, the change in complex viscosity (% ∆η * ) determined by time sweep tests is utilized as input since it indicates thermal stability. Candal et. al. showed that a minimized melting enthalpy or specific volume change are favourable to prevent warpage or geometrical instability. Inspired by that study, the crystallization enthalpy (∆H c ) determined by DSC measurements was utilized as input. Finally, printing parameters namely, extruder temperature (T), and extrusion flow (F) were also utilized as input. The over-/under-extrusion and geometrical features of the printed samples were determined and these values were introduced as output. The random forest (RF) machine learning algorithm, which is based on an ensemble of multiple decision trees was utilized (1) to predict the extrusion and geometrical features and (2) to determine the importance of the material features for the printability and final properties. The RandomForestRegressor class from the sklearn.ensemble package in Python was employed for this purpose . Initially, the dataset was randomly split into a training set (85%) and a test set (15%). The hyperparameters of the prediction model are number of trees in the forest (n estimators), the maximum depth of the tree (max depth), the minimum number of samples required to split an internal node (min samples split), the minimum number of samples required to be at a leaf node (min samples leaf), and the number of features to consider when looking for the best split (max features) .