This study presents a multivariate and machine learning-based approach to analyze quality alerts in an industrial manufacturing context. Based on data from recorded quality alerts, this research integrates exploratory data analysis, Multiple Correspondence Analysis (MCA), and Random Forest modeling to uncover hidden patterns among key categorical variables, including process, section, and priority. The analysis highlights structural associations and frequency distributions that differentiate alert behavior across various production units. Visualization tools such as heatmaps and bar charts are employed to provide actionable insights into the operational environment. The study has practical applications in the monitoring and continuous improvement of quality management systems in manufacturing environments. Identifying patterns in quality alerts through multivariate and machine learning techniques leads to a deeper understanding of the origin and frequency of quality issues across machines, processes, and plant sections. The findings can support preventive actions, efficient resource allocation, and targeted maintenance strategies, ultimately enhancing product consistency.