This study investigates the performance of active infrared thermography for detecting surface and subsurface defects in two commonly used polymers, PLA and ABS. To enhance detection accuracy and image contrast, two advanced image-processing techniques—Principal Component Analysis (PCA) and Pulsed Phase Thermography (PPT)—were applied individually and in combination to the thermal image sequences. Results show that PCA efficiently reduces noise and emphasizes dominant thermal patterns, providing superior identification of shallow defects. In contrast, PPT, through frequency-domain analysis, exhibits lower sensitivity to non-uniform heating and emissivity variations, enabling more reliable detection of deeper defects. Comparative thermal behavior analysis revealed that PLA, due to its higher thermal conductivity, identifies surface defects more rapidly and with greater contrast, whereas ABS, with its higher specific heat capacity, offers improved stability and precision in subsurface defect detection. Moreover, the hybrid application of PCA and PPT proved sequence-dependent: in PLA, the PCA→PPT approach yielded a balanced visualization of both surface and subsurface anomalies, while in ABS, the PPT→PCA sequence significantly enhanced subsurface defect contrast. These findings demonstrate that combining complementary thermographic processing methods can overcome the limitations of single techniques and, when tailored to material-specific thermal properties, provide a robust framework for non-destructive evaluation of additively manufactured polymer components.