This study leverages machine learning to analyze the cross-sectional profiles of materials subjected to wire-rolling processes, focusing on the specific stages of these processes and the characteristics of the resulting microstructural profiles. The convolutional neural network (CNN), a potent tool for visual feature analysis and learning, is utilized to explore the properties and impacts of the cold plastic deformation technique. Specifically, CNNs are constructed and trained using 6400 image segments, each with a resolution of 120x90 pixels. The chosen architecture incorporates convolutional layers intercalated with polling layers and the “relu” activation function. The results, intriguingly, are derived from the observation of only a minuscule cropped fraction of the material’s cross-sectional profile. Following calibration and training of two distinct neural networks, we achieve training and validation accuracies of 97.4%/97% and 79%/75%, respectively. These accuracies correspond to identifying the cropped image’s location and the number of passes applied to the material. Further improvements in accuracy are reported upon integrating the two networks using a multiple-output setup, with the overall training and validation accuracies slightly increasing to 98.9%/79.4% and 94.6%/78.1%, respectively, for the two features. The study emphasizes the pivotal role of specific architectural elements, such as the rescaling parameter of the augmentation process, in attaining a satisfactory prediction rate. Lastly, we delve into the potential implications of our findings, which shed light on the potential of machine learning techniques in refining our understanding of wire-rolling processes and guiding the development of more efficient and sustainable manufacturing practices.