<p>Additive manufacturing (AM) is an emerging technology used to fabricate complex structures with minimal material waste and reduced production cost. Fused Deposition Modeling (FDM) is widely used for fabricating sustainable polylactic acid (PLA) components due to its simplicity. Although AM offers several advantages, one of the major limitations of AM techniques is surface irregularities, which affect both the performance and appearance of printed objects, especially in biomedical applications. The present research focuses on the combined optimization of printing temperature, layer height, and printing speed for PLA-based FDM components using Central Composite Design (CCD)-based Response Surface Methodology (RSM) with detailed statistical validation of the individual and interactive effects on surface roughness. A SUNLU S8 Pro printer was used to fabricate the specimens, and surface roughness measurements were carried out using an SJ-410 tester. Each sample was measured over five iterations, and the average value was used for analysis. A quadratic regression model was developed to establish the relationship between process parameters and surface roughness. The results indicate that layer height has the most significant influence on surface roughness, followed by printing speed, while printing temperature has the least effect. The developed model demonstrated high accuracy, with an R² value of 0.966. The optimal combination of process parameters for achieving minimum surface roughness was identified within the selected range. The study provides a statistically validated predictive framework for process optimization and contributes to improving the surface quality and application potential of FDM-fabricated PLA components in engineering and biomedical fields.</p>