Gut microbiome research has made tremendous progress, especially with the integration of machine learning and artificial intelligence that can provide new insights from complex microbiome data and its impact on human health. The use of explainable artificial intelligence is becoming critical in medicine, and adopting it in gut microbiome prediction models is appealing for providing more transparent and trustworthy models in clinical research. This scoping review evaluates the use of machine learning and explainable artificial intelligence techniques and identifies existing gaps in knowledge in this research area to suggest future research directions. A systematic search was conducted in PubMed and Scopus to include 59 articles published between 2018 and 2023. Different clinical applications of machine learning and artificial intelligence techniques in gut microbiome studies were applied in the reviewed articles, with Random Forest as the most popular and the most used algorithm, and a large prevalence of black box non explainable models. Finally, not enough attention was paid to the reproducibility of the research work published. This review presents the opportunities that remain to advance research regarding the explainability of artificial intelligence models in the field of microbiome, supporting and accelerating the application of microbiomebased precision medicine in the future.