This study introduces a dynamic pricing optimization framework for E-commerce, integrating reinforcement learning, predictive analytics, federated learning, privacy-preserving transaction processing, and blockchain interoperability. Through an extensive ablation study and comprehensive evaluation, the proposed framework outperforms existing methods in multiple dimensions. Figures illustrate its consistent excellence, highlighting its versatility in adapting to market dynamics. Blockchain and machine intelligence should also be utilized to authenticate price information and improve pricing judgments. This study presents a blockchain system that employs machine learning to address online shopping pricing changes This framework not only optimizes pricing strategies but also ensures transparency, security, and personalized customer experiences, making it a robust solution for the dynamic E-commerce environment.