Quality is a critical factor in the automotive industry, as meeting high standards is essential for producing safe and reliable products that enhance a company's competitiveness in the global market. To achieve this, many companies are increasingly adopting new technologies in their production and quality control processes. This thesis investigates the application of artificial intelligence in the automotive industry, specifically focusing on the benefits of integrating machine learning techniques with traditional quality control tools. The research was conducted during the internship period at Pianezza site of the Japanese multinational company Minebea AccessSolution, which is specialized in the production of automotive components, particularly door handles.