Radiographic inspection is essential for ensuring the structural integrity and quality of welded joints in various industries, including manufacturing, construction, and aerospace. Traditional methods often depend on human interpretation, which can result in variability and the potential for overlooking critical defects. This chapter examines the transformative impact of intelligent decision support systems (DSS) driven by deep learning in improving the accuracy and efficiency of radiographic inspections. It begins by underscoring the significance of identifying weld defects, such as porosity and cracks, while also addressing the limitations inherent in manual inspection processes. The chapter delves into the application of various deep learning techniques, highlighting their effectiveness in automated defect detection and predictive maintenance. Case studies illustrate notable enhancements in inspection efficiency and reliability compared to conventional methods, emphasizing how these intelligent systems optimize workflows and uphold quality standards. Finally, the chapter discusses emerging trends, including the integration of multi-modal imaging data and hybrid AI models, along with advancements in interpretability that facilitate better collaboration between human experts and AI, thereby shaping the future of weld inspection practices.