In this work, we perform a detailed bibliometric analysis of machine learning in biochemistry, to critically trace the developments and trends of this revolutionary tool from 1985 to 2023. The evolution of machine learning in biochemistry research is examined using data from Scopus database with a focus on trends in publications, primary authors, institutions, and funding sponsors. The analysis shows a marked increase in machine learning publication activity, especially after 2010, fueled by advances in computational resources, the presence of large datasets and the emergence of state-of-the-art algorithms. The results highlight the global scope of machine learning research in biochemistry, as well as the leading role of the US, China, and Europe. Protein structure prediction, drug discovery, and metabolic pathway analysis are some important areas of research. Thus, this research can benefit researchers and biochemists because it emphasizes that machine learning can be used to tackle the issues of biochemistry up to date and highlight the challenges that biochemistry is facing and what direction it should take.