Rapid prototyping has solidified its position as a cornerstone of contemporary manufacturing. A critical aspect of this technology lies in the precise control of fabrication process parameters. These parameters exert a profound influence on the resultant product’s quality, mechanical, and dimensional accuracy. However, the existing body of research in this area remains in-complete, underscoring the necessity for further investigation. In response to this gap, the exploration and implementation of intelligent algorithms, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), are deemed essential for the precise determination of optimal process parameters. These methodologies not only facilitate the refinement of fabrication processes but also serve as a pivotal conduit for the rapid and effective integration of advancements stemming from the Fourth Industrial Revolution. This study employed a Face-centered Central Composite Design (FCCCD) with five input parameters to systematically vary fabrication settings. Test samples, produced via a desktop DLP resin 3D printer, were used to train and evaluate predictive models (ANFIS, ANN). Results showed all models achieved good accuracy, but ANFIS demonstrated superior performance by synergistically combining ANN’s learning ability and Fuzzy Logic’s inferential capabilities. This highlights the potential of hybrid intelligent systems for optimizing rapid prototyping processes.