This work explores the convergence of blockchain technology, reinforcement learning (RL), supply chain management (SCM), and inventory management to enhance supply chain performance, efficiency, and innovation. We begin with a brief literature review on these topics to establish a taxonomy for our research. RL algorithms can dynamically optimize inventory levels, order fulfillment, and overall supply chain efficiency by leveraging past experiences and feedback. Blockchain technology enables stakeholders to record and verify transactions, enhance traceability, and streamline supply chain processes. Integrating blockchain's data integrity and transparency with RL's adaptive decision-making capabilities enables the development of intelligent, self-learning supply chain systems that enhance supply chain visibility, improve demand forecasting accuracy, and increase resilience. RL can suggest inventory levels that lead to reduced stockouts, lower carrying costs, and increased service levels. The convergence of blockchain, RL, SCM, and inventory management offers transformative opportunities to enhance operational efficiency and drive innovation in the global marketplace.