  Machine learning (ML) has transformed numerous fields, but understanding its
foundational research is crucial for its continued progress. This paper
presents an overview of the significant classical ML algorithms and examines
the state-of-the-art publications spanning twelve decades through an extensive
bibliometric analysis study. We analyzed a dataset of highly cited papers from
prominent ML conferences and journals, employing citation and keyword analyses
to uncover critical insights. The study further identifies the most influential
papers and authors, reveals the evolving collaborative networks within the ML
community, and pinpoints prevailing research themes and emerging focus areas.
Additionally, we examine the geographic distribution of highly cited
publications, highlighting the leading countries in ML research. This study
provides a comprehensive overview of the evolution of traditional learning
algorithms and their impacts. It discusses challenges and opportunities for
future development, focusing on the Global South. The findings from this paper
offer valuable insights for both ML experts and the broader research community,
enhancing understanding of the field's trajectory and its significant influence
on recent advances in learning algorithms.
