Abstract: Infrared thermal imaging technology offers advantages such as fast detection speed, high sensitivity, good visual clarity, and broad applicability, making it an indispensable non-destructive testing method in fields such as aerospace, rail transportation, and energy equipment. However, traditional infrared inspection methods are limited by factors such as low signal-to-noise ratios, faint defect features, and heavy reliance on manual interpretation, and thus still face significant challenges in terms of detection accuracy, automation levels, and quantitative assessment capabilities. With the rise of artificial intelligence, deep learning, with its powerful image feature recognition capabilities, has introduced new solutions to this field. This paper provides an indepth analysis of the core technical challenges currently facing infrared thermal imaging inspection. Focusing on four major technical pathways, namely deep learning-based infrared image enhancement and reconstruction, object detection, semantic segmentation, and defect classification—this paper thoroughly examines the research progress and relative strengths and weaknesses of each approach. Based on this analysis, the paper summarizes the current application status of this technology in key fields such as microelectronic packaging, composite material evaluation, and fatigue crack monitoring. It also outlines future development directions, including lightweight model design for industrial real-time online inspection, three-dimensional quantitative modeling, and physical information fusion, aiming to provide a theoretical framework to facilitate the transition of infrared non-destructive testing toward intelligent and quantitative methods.