This research investigates various classification methods to address challenges in managing RFID data streams within the inventory management. In recent years, there has been a rise in the adoption of advanced technologies and operational management strategies aimed at improving the efficiency and reliability of inventory management systems. One such technology is RFID sensors, enabling real-time identification, tracking, and security of products. However, RFID technology presents challenges such as reliance on continuous internet connectivity, potential system vulnerabilities, and costly solutions for power disruptions. This research explores advanced analytics beyond data summaries to address these challenges. Artificial neural networks (ANN), k-nearest neighbours (KNN), and recurrent neural networks (RNN) were employed for data classification. Notably, ANN achieved an accuracy of 91.44% emerging as the most promising method. The analysis uncovered inaccuracies in location data, with most errors occurring at certain time. These findings contribute to the development of improved operational management strategies, optimize inventory performance and enhancing supply chain operations.