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The new industrial revolution called Industry 4.0 is proliferating at its peak. The time is no longer away when the human race is going to witness a huge paradigm shift. Intelligent machines empowered by Artificial Intelligence (AI) will take over the presence of human workers in the industrial manufacturing sector with the target of achieving 100% automation. With the emergence of cut-throat price competition in the product market, it has become equally important to manufacture goods at minimal costs and with the highest quality. Predicting the decrease in machinery efficiency at an earlier stage to accomplish this objective helps to reduce the failure of the system earlier and at a lower cost. As most of these machineries constitute of bearings, early fault detection in bearings has always been a major goal for the manufacturing industry. Recently researchers have explored the power of AI for fault diagnostics in bearings and it has shown promising results. Therefore, in this paper, the authors present an extensive bibliometric study of the research carried out for fault detection in bearings using Artificial Intelligence. The study focuses on 4314 extracted literature from Scopus database in the form of scientific documents such as journals, articles, book chapters over a period of 2010-2020. This paper will give an in-depth view of the research trends in the domain of bearing fault detection.