<p>Data’s increasing complexity and uncertainty across domains continue to drive the demand for more robust, efficient, and accurate computational methods, including machine learning algorithms for pattern recognition and classification problems. Kohonen’s Learning Vector Quantization algorithms have been integral to classification algorithms for decades. However, variants such as the Generalized Matrix Learning Vector Quantization have emerged as highly promising and capable computational models for analyzing complex patterns in high-dimensional and noisy datasets with increased performance in recent literature. As a result, this systematic literature review attempts to comprehensively examine recent studies on Generalized Matrix Learning Vector Quantization algorithms, focusing on algorithmic enhancements and variations, inherent features like feature relevance and metric learning, application domains, and performance. Using the Denyer and Tranfield 5-stage systematic literature review method, 61 studies published between 2015 and 2024 are selected for analysis from Scopus, Web of Science, IEEE, and Sprinter. The findings reveal significant advancements and applications of the Generalized Matrix Learning Vector Quantization across healthcare, bioinformatics, and agriculture. The analyzed empirical studies highlight the algorithm’s adaptability to various classification problems and enhanced performance. While the cross-disciplinary potential for Generalized Matrix Learning Vector Quantization is well documented, the review identifies gaps in the literature, particularly in the manufacturing domain. Given the rapid advances in manufacturing and the voluminous amounts of data generated, Generalized Matrix Learning Vector Quantization holds great potential in advancing intelligent decision-making across the domain, such as in the selection and management of manufacturing processes.</p>