Undoubtedly, fused filament fabrication (FFF), a class of additive manufacturing has established as one of the most efficient process for the development of a wide range of functional/non-functional products. However, the process suffers from poor geometrical and mechanical characteristics that are limiting its potential for the fabrication of sophisticated parts. In the present work, an effort has been made to investigate the effect of build orientation angle, head scan speed, and layer thickness on the shrinkage of resulting poly-lactic-acid (PLA) prints by using design of experimentation and machine learning based statistical methodologies. Firstly, a total of 20 test samples, with repletion of 5, were prepared by using an open source based commercial fused filament fabrication (FFF) setup and then investigated for their dimensional accuracies by using Coordinate Measuring Machine (CMM). Finally, the obtained data was investigated, statistically, in order to find out the significance of selected process variables on resulted dimensional features. The regression models developed for the prediction of shrinkage by using Statistical and artificial neural network were possessed R² ∼ 97.66% and ∼ 97.78%, respectively. Further, scanning electron micrographic analysis has been carried out to observe the changes occurred in the geometrical features of the produced prints. Overall, the study concluded that rate of angular shrinkage increased with an increase in the build orientation angle and decrease in the layer thickness & head scan speed.