A bibliometric investigation examines machine learning (ML) applications in biomedical engineering through thorough analysis of its evolution from 2012 to 2025 inclusive together with publishing trends and substantive impact on the field. An analysis based on Scopus data evaluates the evolutionary changes in biomedical engineering ML research through publication statistics and identification of major authors and institutions and national profiles. The analysis demonstrates rapid expansion of ML research since 2015 which results from enhanced computational power combined with abundant available datasets alongside advanced algorithm development. The research outcomes demonstrate that ML investigations have broad international involvement because the United States, China and the United Kingdom produce essential research outputs. Research in medical imaging applications and wearable device and predictive analytics forms the main focus of investigators. This research gives essential information to researchers and biomedical engineers and policymakers about how ML addresses current biomedical engineering issues while defining future study paths.