Electronic Nose (ENose) technology has emerged as a transformative tool in medical diagnostics, leveraging sensor arrays that mimic the human olfactory system to detect odors and volatile organic compounds (VOCs) in various biological samples. ENose systems utilize a range of sensor types, such as metal oxide semiconductors and conducting polymers, to generate unique “smell fingerprints” through pattern recognition algorithms. These systems have shown promise in diagnosing various medical conditions, including respiratory diseases, infectious diseases, metabolic disorders, and neurological conditions. Notably, ENose technology holds significant promise in cancer diagnostics, offering a non-invasive, cost-effective, and rapid approach to early detection and monitoring. It has demonstrated impressive accuracy (85%-95%) in detecting cancers and monitoring complications. However, challenges remain, including issues with standardization, sensor sensitivity, and data interpretation. Despite these hurdles, ENose technology’s market growth is fueled by the increasing prevalence of chronic diseases. Recent developments in Artificial Intelligence (AI), particularly machine learning techniques like deep learning, have enhanced the diagnostic accuracy and robustness of ENose devices. This paper explores the evolution, core principles, applications, challenges, and future potential of ENose technology, with particular emphasis on integrating recent advancements in AI for enhanced detection and interpretation. Future research and collaboration across sectors are essential to overcome existing challenges and integrate ENose into mainstream healthcare.