<p>Surface roughness is a critical parameter in assessing the quality of manufactured parts. This study evaluates effective wavelet filters for surface roughness assessment of 3D-printed Polylactic Acid (PLA) components using image processing techniques. PLA specimens were fabricated under controlled conditions, and their surface profiles were acquired using both a Talysurf contact-based stylus profiler and a 3D non-contact roughness tester (Bruker). High-resolution white-light images of the specimen surfaces were captured using a CMOS camera. One-dimensional signal vectors were extracted from the white light image, and Mean Squared Error (MSE) was evaluated to determine the optimal decomposition level. At the same time, the Energy Entropy Ratio (EER) guided the selection of the most effective wavelet filters. The effectiveness of each filter was validated through statistical correlation between wavelet-derived features and surface roughness parameters. EER reveals that Daubechies (db2), Symlet (sym5), Coiflet (coif5), and Biorthogonal (bior1.1, bior1.5, and bior6.8) filters achieved the highest EER values (0.74–0.78) at the 5th decomposition level, demonstrating superior performance in capturing surface texture details. The mean and standard deviation (SD) intensity of effective wavelet filters from statistical analysis are computed and correlated with stylus parameters (R_a, R_da, and R_dq) and 3D roughness parameters (S_a, S_ku, S_z) to validate their effectiveness in characterizing surface roughness. The mean intensity from the detailed coefficients showed strong correlations with stylus parameters and 3D non-contact parameters, with the R² value of 0.80–0.84.</p>