The electronics industry is undergoing a significant transformation due to the integration of machine learning (ML) techniques. ML algorithms enhance various aspects of electronics manufacturing, including predictive maintenance, quality control, defect detection, and supply chain optimization. This paper explores the applications of ML in the electronics sector, reviews existing literature, and presents a methodological framework for implementing ML solutions. A case study on defect detection in printed circuit board (PCB) manufacturing is discussed, demonstrating how ML models improve accuracy and efficiency. The results indicate that ML-driven approaches reduce production costs and enhance product reliability. The study concludes with recommendations for future research and industry adoption.
