Microsectioning is a destructive testing method extensively employed in the flexible printed circuit board (FPC) fabrication industry to assess the structural integrity and quality of FPCs. FPCs are essential components in various electronic devices due to their flexibility, lightweight nature, and ability to fit into complex shapes and spaces. A cross-section, or microsection, involves obtaining a thin slice of the FPC to expose its internal structure. During cross-section analysis, operators manually measure the thickness of FPC components, such as copper layers, OSC layers, and FSL layers. However, this manual process can lead to inconsistencies and difficulties in establishing standardized measurement procedures. To address these challenges, we propose an “AI-based Microsection Measurement Framework using ComfyUI Workflow” for FPC. This framework comprises five key modules: the target detection module, the image preprocessing and augmentation module, the AI model building and fine-tuning module, the measurement algorithm development module, and the ComfyUI visualization module. The measurement algorithm uses predicted masks from the AI model to perform precise measurements, while the visualization module plots these results directly onto the original image for easy review. In addition, we evaluate the proposed framework on two microsection types. Our experiments demonstrate that the measurement accuracy reaches an error margin of 0 pixels. Compared to the existing method, we provide a unified, faster, and lower labor cost measurement framework.