Additive Manufacturing (AM) is an industrial process that involves creating three-dimensional (3D) parts based on computer-aided design (CAD) models. Various methods and techniques have been developed in the recent decade to enhance this industry. This research observes the influence of 3D printing parameters using fused deposition modeling (FDM) on the uniaxial compressive strength (UCS) of polylactic acid (PLA) specimens. This is precisely to study the effects of infill density, infill pattern, and layer thickness and determine the optimal parameters. The compression test samples have been designed based on ASTM D695 standards and manufactured using a Creality Ender-5 Pro 3D printer. Then, a Taguchi design of experiments method has been used, and nine experiments have been conducted to evaluate the effects of the mentioned parameters. Also, analysis of variance (ANOVA) declared that the infill density is the most noticeable parameter with a contribution of 83.56% to the variation in UCS. On the other hands, both infill pattern and layer thickness had minimal impact. However, the ideal configuration to earn maximum UCS value has been recorded as 80% infill density, a gyroid infill pattern, and a 0.3 mm layer thickness based on ANOVA analysis. Furthermore, an artificial neural network (ANN) model has been developed to enhance predictive capabilities. This is by training a three-layer architecture with inputs of infill density, infill pattern, and layer thickness. It is confirmed by two calculation outcomes that the ANN has performed high predictive accuracy: a regression coefficient (R) of 0.9974 and slight deviation between experimental and predicted UCS values. These results show the considerable role of infill density in increasing the compressive strength, as well as approve the ANN as a trusted tool for predicting mechanical properties of 3D-printed components. This research presents profound investigation for optimizing FDM parameters to enhance the mechanical performance of 3D-printed parts.