Artificial Intelligence (AI) has significantly advanced diagnostic capabilities in microbiology labs by automating and enhancing various processes. AI-powered automated microscopy allows for rapid and accurate identification of microorganisms, such as bacteria and parasites, by analyzing microscopic images with deep learning algorithms, reducing the need for manual interpretation. AI also plays a crucial role in genomic data interpretation, particularly in analyzing Next-Generation Sequencing (NGS) data to identify pathogens and predict antibiotic resistance, facilitating personalized treatment strategies. Additionally, AI's predictive analytics capabilities help anticipate outbreaks and monitor antibiotic resistance, enabling proactive public health responses. In microbiology labs, AI-driven automation improves efficiency by handling routine tasks, while AI's ability to reduce human error and enhance diagnostic accuracy ensures consistent and reliable results. The integration of AI in microbiology not only speeds up diagnostic turnaround times but also supports point-of-care diagnostics, providing timely insights for critical treatment decisions. Despite these advancements, challenges such as data quality, bias, ethical considerations, and the need for robust regulatory frameworks remain. Looking forward, the continued evolution of AI promises to further enhance diagnostic precision and support personalized medicine, transforming the future of infectious disease management.