Progressing microbial genomics: Artificial intelligence and deep learning driven advances in genome analysis and therapeutics

The fusion of artificial intelligence (AI) and machine learning has revolutionized microbiology from an empirical science into a data-driven discipline. With the emergence of high-throughput sequencing and multi-omics platforms, microbiologists and clinicians can depend on computational tools to interpret complex data sets, predict antimicrobial resistance (AMR), and design novel therapeutic strategies. The review intends to provide a detailed analysis of AI/ML applications in microbiology, pointing out their roles in genomics, metagenomics, AMR detection, microbial ecology, and CRISPR-based genomic editing in the field of health care settings. It illustrates the recent innovations, practical tools, and challenges in implementing intelligent systems in biomedical microbiology. A structured evaluation was conducted on the present literature databases and AI-driven bioinformatics tools and focused on deep learning models and stochastic methods, specifically on algorithms used across genomic analysis, microbial research, and resistance prediction workflows. AI has empowered rapid genome annotation, functional gene prediction, and identification of biosynthetic gene clusters. ML helps in taxonomic classifications, inference of metabolic pathways, and modeling of synthetic microbiomes. By using AI, about 860,000 novel antimicrobial peptides were identified, and most of them were validated through experiments. Tools such as MG-RAST, antiSMASH, ResFinder, and CRISPR-SID have improved microbial identification and use in clinical settings, giving a mark on the function of the CRISPR-Cas system through deep learning. Even the interactions of microbes, their adaptations, and their potential for bioremediation have been proved through AI models. However, these advancements encounter challenges such as model bias, data heterogeneity, lack of transparency, and infrastructure limitations. Addressing the present challenges through explainable AI (XAL), governance of ethical data, and enhanced computational infrastructure will be a safe and effective use of intelligent technologies in this field.