Background: The advancement of artificial intelligence (AI) in medicine is associated with applications meant to support physicians in diagnosing patients, selecting treatments, and forecasting their results. Thanks to the quick advancement of analytical tools and the growing availability of healthcare data, it is revolutionizing the healthcare industry. Modern deep learning (DL) and machine learning approaches for structured data, such as neural networks and classical support vector machines, as well as natural language processing for unstructured data, are examples of artificial intelligence techniques. Methodology: More than 60 articles were reviewed, and 45 were selected. The literature review was conducted using the databases PubMed, Embase, Google Scholar, and Scopus. Review: Laboratory medicine uses modern technology to improve clinical decision-making, disease monitoring, and patient safety. Artificial intelligence is increasingly used in clinical microbiology informatics. Genomic data from isolated bacteria, metagenomic microbiological results from original specimens, mass spectra recorded from growing bacterial isolates, and huge digital images are all examples of massive datasets in clinical microbiology that can be used to develop AI diagnoses. Conclusion: Healthcare technological innovation is accelerating and becoming more integrated into our daily lives and medical practices, such as smart health trackers and diagnostic algorithms.