The study and treatment of human infection continues to be examined in relation to generative AI, deep learning, machine learning, and AI (artificial intelligence). We provide an overview of AI’s current and prospective uses, as well as how it relates to clinical infection control. Research studies, Clinical trial, and meta-analytic received precedence when screening 1617 the PubMed database findings. The review’s narrative format centres on investigations that employ clinically validated prospectively acquired data from the real world, as well as studies with transformative possibility, including unique pharmaceutical discovery and microbiome-based interventions. Clinical imaging analysis (e.g., tuberculosis of the lungs diagnosis), tools for clinical decision-support (e.g., antimicrobial recommending, sepsis prediction), digital culture plate reading, antimicrobial resistance profiling, and malaria diagnosis are a few areas where there is proof to support the medical value of artificial intelligence (AI) used for diagnostics in laboratories. To date, most studies have not included medical metrics or real-world validation. Comparability is hampered by substantial variation in research methodology and reports. There are a lot of practical and ethical concerns, such as bias risk and algorithm transparency. Though the practical medical value of artificial intelligence (AI) tools for infections investigation and control seems to be much more modest, enthusiasm for the research and creation of these tools is certainly gaining momentum.