As supply chain management continues to evolve, efficient inventory management systems have become increasingly crucial. However, traditional manual methods often struggle to meet the complexities of modern market demands, particularly when it comes to data accuracy, delays in monitoring, and the heavy reliance on subjective experience for forecasting. This study introduces an intelligent, semi-automated barcode-based inventory management system designed to overcome these challenges. The system integrates barcode technology with a distributed architecture, combined with big data analytics and machine learning for real-time tracking and accurate inventory predictions. Its performance has been validated through multiple simulation tests, where it has outperformed traditional RFID technology in certain cases. Through careful system design, technology exploration, and validation, this research demonstrates the significant potential of this intelligent system in improving inventory management efficiency and accuracy