Adulteration of honey poses a significant threat to both consumer health and the honey industry, necessitating the development of reliable detection methods. This study explores the efficacy of electronic nose (e-nose) technology in identifying adulterated forest honey. Honey samples were collected from Periyar Wildlife Sanctuary, Thekkady, India, comprising both original forest honey and samples that were intentionally adulterated. Twenty samples each of authentic honey and adulterated honey were used in this study. The honey samples were adulterated with sugarcane syrup (Jaggery). The e-nose system employed in this research consisted of eight metal oxide semiconductor (MOS) gas sensors designed to detect volatile organic compounds associated with the distinct odor profiles of the honey samples. To enhance the accuracy of odor detection, a temperature-controlled chamber was developed, incorporating a Nichrome heater wire, a TEC Peltier cooler, LM35 Temperature Sensor, Arduino UNO, and a 5V-4 Channel relay module, ensuring optimal environmental conditions for the e-nose. In our methodology, the e-nose was used to capture odor signatures from both pure and adulterated honey samples within this controlled environment. The collected data were subjected to preprocessing before being analyzed using machine learning algorithms, specifically k-nearest neighbors (kNN) and support vector machine (SVM). These algorithms were chosen for their robust classification capabilities and suitability for pattern recognition tasks. Our findings indicate that the e-nose system, combined with these machine learning techniques, achieved an impressive accuracy of 98% in distinguishing between pure and adulterated honey. This high accuracy demonstrates the effectiveness of the e-nose technology in capturing subtle differences in odor profiles, which are crucial for detecting adulteration. The successful differentiation of honey samples suggests that the e-nose can serve as a valuable asset in the fight against honey adulteration, ensuring product integrity and consumer trust.