Urinalysis is a significant diagnostic tool for the detection of various diseases. The recent surge in the applications of artificial intelligence (AI) has revolutionized the medical industry, including urine analysis. AI has become an indispensable tool in clinical decision making, enabling the identification of illnesses, accurate diagnosis, and personalized therapy and management of various diseases. The analysis of urine encompasses the assessment of several components, including proteins, electrolytes, and creatinine, which may undergo modifications contingent upon the physiological and pathological condition. The advancement of urine detection methodologies, including urine proteomics, metabolomics, and RNomics, has facilitated the retrieval of diverse data from this readily accessible and abundant source. However, the utilization of this resource has been a challenge due to the sheer amount of data that needs to be processed and analyzed. AI optimization of urine data processing has solved the utilization challenge. AI algorithms can analyze large amounts of urine data quickly and accurately, enabling non-invasive and precise illness detection and therapy using urine. AI-based urine detection has been used for various diseases, including kidney disease, urinary tract infections, and prostate cancer. Despite the promising prospects of AI-based urine detection, there are still challenges to be addressed. The challenges encompass several key aspects, especially the requirement for larger and more comprehensive data sets, the advancement of AI algorithms with enhanced precision, and the establishment of standardized protocols for urine sample collection and processing. By effectively tackling these problems, the complete potential of AI-driven urine detection can be actualized. This review examines the utilization of artificial intelligence (AI) in urine detection for the purpose of disease diagnosis and treatment. It emphasizes the potential benefits, problems, and prospects associated with this approach. This paper investigates different technologies utilized for urine detection, the integration of artificial intelligence (AI) in the processing of urine data, and the clinical applications associated with AI-based urine detection. The article finishes by providing an analysis of the obstacles and potential opportunities associated with AI-driven urine detection, emphasizing the necessity for additional research in this domain.