Gut microbial profiles induce cancer growth and impact treatment effectiveness, tolerance, and safety. There is still more to discover about the relationship between diseases and the microbiota and its clinical consequences. Even though much of the study is still in its early phases, the 'omics' technologies were widely used for microbiome analysis due to the increased size of datasets available in public databases. However, recognizing the potential of these new technologies is difficult at times, limiting our ability to analyze a vast amount of available data critically. In this context, two subsets of AI methods, Machine Learning (ML) and Deep Learning (DL), can aid clinicians in analyzing and comprehending these large datasets. Here, we reviewed the most up-to-date ML methodologies, databases, and tools used in human microbiome research. The proposed review forecast the use of ML in microbiome research involving associations and clinical applications for diagnostics, prognostics, and therapies.