The emergence of advanced manufacturing techniques has driven the need for sophisticated quality assurance and control mechanisms in the production of machined metal parts. Residual stresses, which are stress distributions that remain in a material after the original cause of the stresses has been removed, are a critical concern in the machining industry. These stresses can significantly influence the mechanical properties, performance, and longevity of metal components. Traditionally, methods for detecting and analyzing residual stress have been invasive, time-consuming, and require bulky equipment. However, with the advent of artificial intelligence (AI) and advancements in sensor technology, there is a transformative shift towards the development of portable, non-invasive, and efficient residual stress detectors.