<p>Oxidative deterioration of edible oils leads to rancidity, resulting in the formation of volatile and non-volatile degradation products that adversely affect sensory quality, nutritional value, and consumer acceptance. Conventional methods for rancidity detection, such as peroxide value, p-anisidine value, free fatty acid value, thiobarbituric acid test, chromatography, and spectroscopic techniques, are accurate but time-consuming, costly, and laboratory-based, making them unsuitable for rapid detection. This review comprehensively analyses the potential of electronic nose (e-nose) technologies for rapid detection of rancidity in various edible oils. An e-nose system typically comprises a sample transfer unit, a sensor array (e.g. metal oxide or polymer-based gas sensors) for detecting volatile oxidation products, and a pattern recognition or data analysis system. Recent developments integrating e-nose systems with artificial intelligence, machine learning, computer vision, and hybrid analytical techniques are critically discussed, demonstrating improved accuracy, sensitivity, and practical applicability. Overall, this review bridges the gap between conventional and advanced analytical approaches by highlighting e-nose technologies as cost-effective, user-friendly, and scalable tools for routine quality assessment. The e-nose thus holds great promise for applications in oil processing industries, retail outlets, restaurants, and even household kitchens, contributing to improved food safety, reduced waste, and informed consumer choices.</p> Graphical Abstract <p></p>