This research takes a case study approach to show the development of a diverse adoption and product strategy distinct from the core manufacturing industry process. It explains the development status in all aspects of smart manufacturing, via the example of ceramic circuit board manufacturing and electronic assembly, and outlines future smart manufacturing plans and processes. This research proposed two experiments using Artificial Intelligence and deep learning are used to demonstrate the problems and solutions regarding methods in manufacturing and factory facilities, respectively. In the first experiment, a Bayesian network inference is used to find the cause of the problem of metal residues between electronic circuits through key process and quality correlations. In the second experiment, a Convolutional Neural Network (CNN) is used to identify false defects that were over-inspected during Automatic Optical Inspection. This improves the manufacturing process by enhancing yield rate and reducing cost. The contributions of the study in circuit board production. Smart manufacturing, with the application of a Bayesian network to an IoT setup, has addressed the problem of residue and redundant conductors on the edge of the ceramic circuit board pattern, and has improved and prevented leakage and high-frequency interference. CNN and deep learning were used to improve the accuracy of the AOI system, reduce the current manual review ratio, save labour costs and provide defect classification as a reference for pre-process improvement.