Efficient material flow management is a fundamental requirement for achieving high productivity, low operational cost, and reliable delivery performance in modern manufacturing and logistics systems. As production environments become increasingly complex and demand variability grows, traditional material handling approaches based on manual tracking, static routing, or fixed scheduling are no longer sufficient. These conventional systems often lack real-time visibility and adaptability, leading to congestion, excessive waiting times, and inefficient utilization of resources. Recent advancements in digital technologies provide new opportunities to address these challenges through intelligent and data-driven solutions. This paper proposes an intelligent material flow optimization framework that integrates Internet of Things (IoT) sensors and Radio Frequency Identification (RFID) tracking to enable continuous monitoring and adaptive decision-making. RFID technology is used to uniquely identify and track materials throughout the system, while IoT sensors collect real-time operational data related to equipment status, movement conditions, and congestion levels. The collected data is processed and analyzed using dynamic optimization algorithms that adjust material routing and scheduling decisions in real time based on current system conditions. The effectiveness of the proposed framework is evaluated through simulation-based experiments under both normal and disturbed operating scenarios. Performance is assessed using key metrics such as material waiting time, system throughput, and equipment utilization. The results demonstrate that the proposed approach significantly reduces material waiting time, improves resource balance, and enhances overall system efficiency when compared to conventional static material flow control methods. These findings highlight the potential of IoT- and RFID-enabled intelligent optimization for smart manufacturing and logistics environments.