Mapping the Landscape of AI and Machine Learning in E-Business: A Global Bibliometric Analysis of Research Trends and Future Directions

This study conducts a comprehensive bibliometric analysis to examine the evolution, intellectual structure, and emerging trends in the application of Artificial Intelligence and Machine Learning within the E-Business domain. Drawing on 7,207 publications indexed in Scopus between 2016 and 2025, the analysis explores publication trends, influential authors, leading institutions, and thematic developments. The findings reveal a significant growth, with a marked increase in research output post-2019. Core contributing journals, prolific scholars, and research clusters were identified using co-authorship, keyword co-occurrence, and citation analyses via VOSviewer. Key thematic areas include electronic commerce, recommender systems, customer behavior analytics, fraud detection, machine learning and deep learning architectures. Methodological advances such as ensemble models, deep neural networks, and reinforcement learning dominate the landscape. Despite the increasing scholarly attention, gaps remain in interdisciplinary collaboration and practical adoption frameworks. The study provides theoretical and practical insights, revealing future research directions in sustainable AI and cross-sector E-Business innovation.