The implementation of advanced manufacturing systems, combining intelligent technologies, has enabled the development of new and significant trends in production environments. One of these trends is the impact of visual inspection on quality control processes, particularly in the automotive industry. This paper presents the application of artificial intelligence methods in an automatic visual inspection process for validating the surface, dimensional, and geometric quality of a cast iron part. Digital image processing and supervised machine learning techniques are combined; the learning process is developed with a hybrid technique (multi-classifier method) called Stacking, which merges the following techniques: Naive Bayes, K-Nearest Neighbor (KNN), Decision Trees, and Support Vector Machine (SVM), obtaining responses in a single model, achieving an impact on the performance of the inspection process and improving the accuracy of part classification. The developed knowledge extraction model combines the scalability and reliability of traditional digital image processing with humans’ innate ability to detect variance. It employs an approach based on input examples generated by the machine learning model to identify the behavior of the dataset, leading to improved decision-making. These algorithms can enhance the quality of inspection processes not only in the automotive industry but also in many other manufacturing applications.