The aim of this study is to identify emerging trends in the application of Machine Learning (ML) techniques for fault detection in real-world industrial environments. To support this analysis, Natural Language Processing (NLP) was employed as a tool for extracting and interpreting relevant information from the literature. The NLP workflow was developed using Python and executed within the PyCharm Integrated Development Environment (IDE). The study focused on peer-reviewed journal articles indexed in the SCOPUS database, which were selected based on keywords related to ML, fault detection, and industrial applications. Through the application of NLP techniques such as text cleaning, tokenization, and frequency analysis, the study sought to uncover prevailing patterns and methods used in the field. The findings reveal that data-driven approaches are central to industrial quality control efforts. Most frequently, classification models are used, with deep learning methods—particularly Convolutional Neural Networks (CNNs) emerging as the most common approach. In terms of performance evaluation, the accuracy metric is by far the most reported. These results highlight a strong reliance on advanced ML models in industry, especially for tasks where image or sensor data are involved, although the study does not explore the theoretical justifications behind this trend.