In OLED production, the glass fabrication process is prone to frequent occurrences of persistent mura defects, leading to significant damage in the event of defects, as the subsequent final inspection, AVI (Auto Visual Inspection), is time‐consuming. Therefore, there is a need for a consistent inspection. Currently, manual inspection by operators introduces variations in inspection criteria among individuals, resulting in ongoing challenges related to false judgments and post‐process leakage issues The invested automated inspection system for quantitative determination is utilized as a reference only, as the distinction between pseudo‐defects deemed acceptable in AVI and true defects identified as faulty in AVI poses limitations with the traditional logic‐based inspection methods. We enhanced the mura visibility of automatic inspection system (Auto Macro) images through pre‐processing, followed by translation into an image environment similar to AVI images using Pix2pix GAN. Subsequently, we compared the mura index extracted from the processed images with the AVI mura index for evaluation. The Auto Macro images transformed by Pix2pix GAN exhibited similarities with AVI images in terms of overall luminance, gray distribution, and mura visibility intensity. Consequently, the R‐Squared correlation between the mura index of Auto Macro and AVI improved from 0.00 to 0.68. Additionally, defects unrecognized in AVI during Auto Macro inspection were automatically eliminated. False defects in the Auto Macro test that were not recognized in AVI were automatically removed. In this study, we applied the AI model to the Auto Macro inspection system to overcome the limitations of logic‐based inspections. We proposed a method to proactively detect defect‐induced muras before cell‐level processing, aiming to improve yield and reduce incidents of customer leakage accidents.