Microbiome analysis has become an important area of study in biomedical
and environmental sciences, offering knowledge regarding microbial communities and
their interactions. Machine Learning (ML) and Deep Learning (DL) have also played a
major role in improving the study of microbiomes by supporting effective processing
and classification of high-dimensional metagenomic data. Here, the application of DL
methods, including Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), and Autoencoders, is explored in the context of disease prognosis,
microbial community typing, environmental microbiome research, and personalised
medicine. The challenges, such as limited labelled data, model explainability, and the
absence of generalisability, persist despite all the progress. The integration of multiomics data, Explainable AI (XAI), and Federated Learning becomes unavoidable in
overcoming these bottlenecks. Future research needs to be focused on developing
standardised datasets and scalable AI models for real-time monitoring of the
microbiome. Overwhelming these encounters, ML and DL will continue to
revolutionise microbiome analysis, foremost to innovation in accurate medicine and
biotechnology, besides ecological sustainability.
Keywords: ANN, Autoencoder, Biotechnology, CNN, Deep learning, Machine learning, Microbiome.
