Conventional microbial diagnostic techniques encounter considerable obstacles, such as prolonged turnaround times, labor-intensive protocols, and constraints in precision. To fix these problems and improve diagnostic capabilities, clinical microbiology is using more and more artificial intelligence (AI) technologies. To systematically evaluate the present applications, developments, and influence of artificial intelligence technologies in clinical and diagnostic microbiology, emphasizing pathogen identification, antimicrobial resistance detection, and laboratory automation. A thorough systematic literature search was performed utilizing the PubMed, Scopus, Web of Science, and Google Scholar databases from 2020-2024. Search terms comprised combinations of “artificial intelligence”, “machine learning”, “clinical microbiology”, “diagnostic microbiology”, “pathogen identification”, and “antimicrobial resistance”. Studies detailing AI applications in clinical microbiology were included, whereas non-English articles and review papers were excluded. Eighty-nine studies met the requirements for inclusion. Machine learning algorithms showed high accuracy (85%-99%) in finding pathogens in different types of samples. Deep learning models outperformed others in predicting antimicrobial resistance, with AUROC (Area Under the Receiver Operating Characteristic) values above 0.83. AI-enhanced microscopy and automated image analysis cut down on the time it took to make a diagnosis from days to hours while keeping the sensitivity (92%-98%) and specificity (81%-95%) high. AI technologies have transformed clinical microbiology by delivering swift and precise diagnostic solutions. Combining machine learning with MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization Time-of-Flight), automated microscopy, and genomic analysis has made it easier to find pathogens and test for antibiotic resistance. AI is a game-changing force in modern diagnostic microbiology, even though it is hard to standardize and use.