This study presents an optimization framework for load balancing and redundancy mechanisms in firewall clustering to achieve high availability and reliability in industrial network systems. Industrial environments rely heavily on uninterrupted connectivity and secure data flow, making firewall clustering essential for mitigating single points of failure and performance degradation. The proposed model introduces a hybrid active–active clustering architecture that integrates adaptive load balancing, flow-aware traffic distribution, and lightweight state synchronization. A GNS3-based emulation testbed was developed to replicate industrial network traffic, including both control and data-plane operations, to evaluate performance under normal and failure conditions. Experimental analysis demonstrates that the optimized clustering approach significantly improves throughput by up to 40% and reduces failover time by 65% compared to conventional active–passive models. Moreover, it ensures session persistence and minimal packet loss during transition events. The research emphasizes efficient resource utilization, rapid recovery, and resilient session management, establishing a scalable and secure firewall clustering model capable of maintaining deterministic network performance for mission-critical industrial systems.