A Bibliometric Insight to Machine Learning Applications for Decision-Making

Abstract

Using a multi-method bibliometric analysis of published documents from Web of Science and Scopus in the last 34 years, this comprehensive study investigates how machine learning improves advanced decision-making while adhering to the PRISMA guidelines. This study's main goal is to make the methodological patterns, thematic directions, and intellectual structure of research at the nexus of machine learning and decision-making visible. The results show that the U.S., China, India, Germany, and the U.K. are leading a rapidly expanding, cooperative research landscape with a strong emphasis on management, marketing, and finance. Tree-based models, support vector machines, deep learning, reinforcement learning, and explainable artificial intelligence are examples of frequently used algorithms. The field is moving toward applications in big data environments, ethical considerations, and increased interpretability. Digital transformation, competitive intelligence, and strategic planning are highlighted in influential works. This synthesis offers direction for developing more transparent machine learning models and practical frameworks for their use in decision-making, serving both academics and practitioners.