Artificial intelligence (AI) in microbial diagnostics represents an established and rapidly evolving approach of analysing the presence of disease within the patient population while providing precise and quick results. Globally, bespoke AI models are developed to analyse microbial samples, make predictions, and establish a link between the pathogen and symptoms. Currently, AI is being used in several areas of microbiology, including virology, parasitology, mycology, and bacteriology, particularly in diagnostics. AI-powered technologies, including machine learning (ML) and deep learning (DL), analyze complex microbial data such as genomic sequences, phenotypes, and clinical metadata, enabling early detection of infectious diseases and personalized treatment strategies. Even with these advancements, there are still significant challenges affecting AI diagnostics at present. Ethical concerns jeopardise patient confidentiality and equitable access to healthcare. AI models’ reliance on high-quality datasets for training leads to technical restrictions and may result in errors when applied to diverse microbial strains or under-represented populations. Additionally, AI systems usually require complex infrastructure and processing capacity, which restricts their application in low-resource contexts. Operational challenges such as too much reliance on automated systems, could compromise human judgement in critical decision-making situations. Despite being slower, traditional techniques offer subtle insights that AI might miss. Furthermore, the ethical and technical challenges of integrating AI in microbiological diagnostics are not adequately addressed by regulatory frameworks and standardisation. In order to maximise the use of AI in microbiological diagnosis, this study emphasises the necessity of addressing those drawbacks. By combining AI with conventional methods responsibly, healthcare can enhance diagnostic precision while reducing the hazards associated with developing technologies.