This paper explores the role of computational parallelism (CP) in the various artificial intelligence (AI) learning models. In the introduction, this paper highlights the evolution and importance of parallelism in AI systems, addressing the central technologies and strategies employed. In the literature review, the authors examine the current state, challenges, and emerging trends in the field, revealing relevant information. In methodology, the authors explained how the data was obtained and how it was treated, including a bibliometric study and analyses. The authors also show the results and discuss the gaps identified in the research and areas that reveal limitations or contradictions. The authors used the WoS and Scopus Databases, which are essential for a comprehensive analysis, to conduct their bibliographic study and understand the current literature landscape in their research area; this approach is important. Their study gives researchers, practitioners, and policymakers an important insight into trends and the main works identified. The bibliometric analysis of the two databases has led to important findings. The authors have identified significant data indicating a growing interest of different researchers in this topic. In particular, these results showed the continuing contributions of several authors in this area. In addition, the analysis revealed a growing interest and potential for further research in this area of study by new authors. The evolution of research in the framework of this study, which offers new perspectives and opportunities for future investigation, is illustrated by such a diverse mix of established and developing researchers.