The burgeoning digital landscape has necessitated advancements in blockchain network optimization, particularly in enhancing privacy without compromising network efficiency. Existing blockchain routing mechanisms often fail to address comprehensive privacy concerns, typically offering suboptimal trade-offs between privacy preservation and network performance. The proposed model encompasses five innovative methods. First, Dynamic Routing with Deep Q-Networks (DR-DQN) leverages deep Q-learning to adapt routing paths dynamically based on network conditions and privacy requirements. It significantly reduces average latency by 20% while maintaining high privacy levels. Second, Autonomous Privacy-Preserving Routing (APPR) combines deep reinforcement learning with differential privacy techniques to autonomously optimize routing decisions, achieving a 25% reduction in information leakage. Third, Privacy-Preserving Graph Analytics for Blockchain Networks (PP-GABN) utilizes graph analytics integrated with differential privacy to analyze and optimize network data, thereby reducing privacy leakage by 30%. Fourth, Homomorphic Encryption-based Secure Routing (HE-SR) employs homomorphic encryption, allowing computations on encrypted data to preserve privacy effectively, achieving near-zero information leakage. Lastly, Bayesian Privacy-Preserving Routing (BPPR) uses Bayesian inference to model probabilistic routing decisions, enhancing privacy levels by 35% with minimal impact on routing efficiency. The convergence of these methods within a unified framework discourses the gaps in privacy and efficiency and sets a new standard for the design of secure, scalable, and efficient blockchain networks. This comprehensive approach provides a robust basis for future study and development in blockchain network optimization.