Ensuring the quality of meat is crucial to prevent health hazards caused by improper handling. To address this issue, a smart packaging system is necessary for continuous monitoring of beef quality and microbial population, benefiting both meat industries and end consumers. The presence of spoilage-causing microbes can be detected using an electronic nose (e-nose), a cost-effective and rapid instrument for beef quality classification. This research introduces the development of a mobile e-nose system for beef quality detection and monitoring. The system comprises a chemical gas sensor array, data acquisition system, data processing system, and pattern recognition system. The gas sensors utilized in the sensor array include MQ135, MQ137, MQ9, MQ3, TGS 2620, TGS 2610, TGS 2600, and TGS 822. The experiment involved a dataset with 1800 data points. The experimental results demonstrate the system's ability to accurately distinguish between fresh and spoiled beef. Furthermore, it exhibits promising classification accuracy for binary, three-class, and four-class classification tasks, achieving 94.11%, 87.72%, and 84.93% accuracy, respectively, using the support vector machine model. Therefore, this system presents a potential solution for a low-cost, user-friendly, and real-time meat quality monitoring system. This research contributes to the development of an accessible and efficient meat quality monitoring system, addressing the need for continuous assessment and ensuring consumer safety.