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In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.