Machine Learning Research Trends in Africa - A 30 Years Overview - with Bibliometric Analysis Review

Sep 17, 2026

The paper examines machine learning (ML) research trends in Africa through a critical bibliometric analysis paired with an extensive literature survey. ML models are framed as widely deployed across fields including natural language processing, pattern recognition, object and image detection, and earth observation. From a regional perspective, ML is linked to addressing challenges such as poverty, education improvement, quality healthcare, and sustainability concerns like food security and climate change. The analysis covers 2,761 ML-related documents from 54 African countries (1993–2021), visualizing the current landscape and future collaboration opportunities.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages31

Reading Time1 h 18 min

AccessFree to download

Abstract

Abbreviations

Introduction

A Brief Background of ML and Its Evolution from AI‑ML‑DL

Archives of Computational Methods in Engineering (2023) 30:4177–4207 [https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1831-023-09930-z Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review Absalom E. Ezugwu1 · Olaide N. Oyelade2 · Abiodun M. Ikotun1 · Jeffery O. Agushaka1 · Yuh‑Shan Ho3 Received: 18 February 2023 / Accepted: 19 April 2023 / Published online: 29 April 2023 © The Author(s) 2023 Abstract The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa’s most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent. Abbreviations CU, Egypt UKZN, South Africa Cairo University, Egypt University of KwaZulu-Natal, South Africa * Absalom E. Ezugwu [absalom.ezugwu@nwu.ac.za](absalom.ezugwu@nwu.ac.za) * Yuh-Shan Ho[ysho@asia.edu.tw](ysho@asia.edu.tw) [Olaide N. Oyelade](Olaide N. Oyelade) [olaide_oyelade@yahoo.com](olaide_oyelade@yahoo.com) Abiodun M. Ikotun [biodunikotun@gmail.com](biodunikotun@gmail.com) [Jeffery O. Agushaka](Jeffery O. Agushaka) [jefshak@gmail.com](jefshak@gmail.com) 1 Unit for Data Science and Computing, North-West University, 11 Hoffman Street, Potchefstroom 2520, South Africa 2 Department of Computer Science, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria 3 Trend Research Centre, Asia University, No. 500, Lioufeng RoadWufeng, Taichung 41354, Taiwan UCT, South Africa MansU, Egypt ZU, Egypt UW, South Africa BU, Egypt MU, Egypt UP, South Africa ASU, Egypt UJ, South Africa UWC, South Africa SU, South Africa HU, Egypt TU, Egypt UTEM, Tunisia AU, Egypt UT, Tunisia University of Cape Town, South Africa Mansoura University, Egypt Zagazig University, Egypt University of Witwatersrand, South Africa Benha University, Egypt Menoufia University, Egypt University of Pretoria, South Africa Ain Shams University, Egypt University of Johannesburg, South Africa University of Western Cape, South Africa Stellenbosch University, South Africa Helwan University, Egypt Tanta University, Egypt University of Tunis El Manar, Tunisia Alexandria University, Egypt University of Tunis, Tunisia SCU, Egypt Suez Canal University, Egypt UC, Tunisia University of Carthage, Tunisia 1 Introduction The evolvement of the development and use of computers in intelligently solving problems predates the creation and test

FAQs

What is the main purpose of the study?

To conduct a critical bibliometric analysis of machine learning research trends in Africa, supported by an extensive literature survey and a focus on future applications and collaboration.

Which data sources and time span are used in the bibliometric analysis?

The analysis is based on documents retrieved from the Science Citation Index EXPANDED, covering research publications from 54 African countries between 1993 and 2021.

What does the study report about the scale and publication profile of ML research?

It compiles 2,761 ML-related documents, with 89% being articles that received at least 482 citations, published across 903 journals during the past three decades.

Sep 17, 2026

Uploaded by Emma Wilson