The human microbiome is a complex ecosystem that influences various functions within the human body. With technological advancements, microbiome studies have expanded, bringing forth the challenge of interpreting large volumes of data, which require robust tools based on artificial intelligence (AI). Subfields of AI, including machine learning (ML) and deep learning (DL), have been applied to analyze complex and large-scale datasets, such as microbiome data, with particular utility in identifying and predicting microorganisms in different health conditions. In the era of Big Data, integrating AI with sequencing techniques allows for a more detailed analysis of microbial data, enabling the detection of complex patterns and prediction of health states. However, AI use in this field still faces challenges, such as data heterogeneity (e.g., different sequencing platforms produce data with varying quality and resolution) and the need for data collection and analysis standardization processes (e.g., lack of standardized protocols for sample collection and data analysis). Despite these challenges, AI has significant potential to revolutionize microbiome research. It can assist in identifying biomarkers for diagnostics and treatments, advancing personalized nutrition and precision medicine. The future of this field will depend on the continued development of technologies and collaboration among multidisciplinary teams.