The medical field is witnessing rapid advancements in the application of artificial intelligence (AI) technologies for pathogen detection and infection diagnosis. Integrating intelligent algorithms with microbial biosensors has emerged as a promising approach that can improve diagnostic accuracy and reduce the time required to obtain results. This development stems from the growing need to establish modern diagnostic methods capable of detecting pathogenic microorganisms at an early stage, limiting the spread of infectious diseases, and supporting more efficient medical decision-making. Microbial biosensors are among the modern technologies that rely on microorganisms or their associated biological components to interact with specific substances or indicators and convert these interactions into measurable and analyzable signals. These biosensors can be used to detect certain types of bacteria, viruses, and fungi, as well as to monitor toxins and biological substances associated with the presence of pathogens, depending on the sensor's nature and design. Such technologies can be incorporated into advanced analytical systems designed to provide rapid and sensitive results in medical laboratories and environmental monitoring and health safety applications. Artificial intelligence plays an important role in enhancing the performance of these biosensors by analyzing the signals and data they generate and identifying subtle patterns that may be difficult to distinguish using conventional methods. Machine learning algorithms can be trained on known datasets to help differentiate signals associated with pathogens from those generated by other factors. They can also improve result interpretation and reduce misclassification. Furthermore, deep learning techniques can be employed to analyze complex data and integrate information obtained from multiple sensors, thereby improving the reliability of detection systems when their performance is tested and validated under different conditions. The importance of this integration is particularly evident in the field of intelligent medical systems, where diagnostic platforms can be designed by combining biosensors, data-processing units, and intelligent algorithms to provide information that helps specialists make informed diagnostic decisions. These platforms can also be connected to health monitoring programs and early warning systems, enabling the monitoring of biological and environmental indicators associated with the spread of infections and the identification of abnormal patterns that may require further testing. However, these systems are not substitutes for confirmatory tests or medical evaluation; rather, they serve as supportive tools whose accuracy and effectiveness must be validated before clinical adoption. Another important aspect is the potential use of artificial intelligence in biosensor development by analyzing sensor performance, identifying factors that influence sensitivity and selectivity, and improving the ability to distinguish between different types of pathogens. This can help researchers understand the relationship between the biological characteristics of microorganisms and the signals generated by biosensors, opening new opportunities for developing diagnostic tools better suited to medical and laboratory environments. Collaboration among microbiology, biomedical engineering, and artificial intelligence specialists can also facilitate the development of integrated scientific solutions that address the growing challenges of infectious disease diagnosis. Despite the promising potential of these technologies, their implementation faces several challenges, including the need for accurate and diverse training data, ensuring biosensor quality and consistent performance, and addressing interference that may affect measurement results. Moving from laboratory prototypes to practical medical applications also requires independent validation studies and assessments of sensitivity, specificity, and false-positive and false-negative rates. In addition, protecting health data, ensuring clear interpretation of results, and complying with established scientific and regulatory standards are essential considerations. In conclusion, employing artificial intelligence to detect pathogens using microbial biosensors represents a promising field that brings together microbiology, modern medical technologies, and intelligent data analysis. Continued research and development in this area are expected to contribute to improving early infection detection, supporting infectious disease surveillance, and enhancing the efficiency of healthcare systems. It also creates opportunities for students and researchers in intelligent medical systems to develop innovative solutions that integrate biological knowledge, engineering, and artificial intelligence, ultimately serving human health and supporting the development of a more advanced and sustainable healthcare system. Al-Mustaqbal University – The First University in Iraq