Clinical microbiology relies heavily on accurate microbial identification. However, the drawbacks of traditional methods, such as sample degradation, ambiguous morphology, extended turnaround times, and resource-intensive workflows, hinder prompt diagnosis and efficient treatment. Artificial intelligence and machine learning, when combined, have revolutionized biomarker discovery, antibiotic resistance prediction, and pathogen detection by offering rapid, accurate, and scalable alternatives. Machine learning and deep learning have made taxonomic assignment, feature extraction, and automated microbial image analysis simpler. However, when paired with next-generation sequencing, MALDI-TOF mass spectrometry, and volatile organic compound profiling, AI-driven algorithms have improved classification accuracy and diagnostic efficiency. By supporting drug repurposing, vaccination target discovery, epidemiological forecasting, and tracking antibiotic resistance, AI enhances personalized care and public health preparedness beyond diagnostics. Furthermore, AI-powered automation expedites laboratory procedures, minimizes human error, and improves resource allocation; however, concerns regarding bias, interpretability, and data quality still need to be addressed. Overall, this chapter demonstrates how AI and machine learning are revolutionizing microbial decoding by addressing traditional diagnostic gaps, accelerating the development of novel therapies, and transforming clinical and epidemiological strategies to combat infectious diseases.