Industry 4.0’s quick development and the introduction of 6G connectivity technologies have increased the need for intelligent, adaptable, and safe control systems in remote industrial settings. This research explores a Blockchain-Secured Multi- Agent Digital Twin and Deep Net Framework (BSMDTDF) to enhance real-time collaboration, system reliability, and operational efficiency across decentralized manufacturing networks. The framework utilizes IoT-driven smart manufacturing datasets with preprocessing for noise reduction and normalization. To create dynamic virtual recreations of physical assets and facilitate real-time monitoring, predictive maintenance, and performance enhancement, the framework makes use of Digital Twin (DT) technology. A Multi-Agent System (MAS) architecture supports decentralized decision-making, allowing autonomous agents to coordinate manufacturing resources and adapt to fluctuating production demands. To ensure data integrity and trust in distributed environments, a Blockchain layer with smart contracts secures inter-agent communication and transaction records. The integration of 6G networks provides ultra-low latency, massive device connectivity, and high-speed data transmission, significantly improving responsiveness and synchronization among distributed agents. Furthermore, Fuzzy PID control enhances process adaptability by dynamically tuning parameters under uncertain and nonlinear operating conditions. To further improve predictive accuracy and control optimization, a Long Short-Term Memory (LSTM) deep learning model is incorporated to learn temporal dependencies and forecast control behavior for enhanced stability and precision. Experimental simulations demonstrate f1-score (98.41%), accuracy (98.56%), recall (98.23%), and precision (99.53%) compared to conventional methods. The BSMDTDF framework establishes sustainable manufacturing through the convergence of blockchain security, DT intelligence, 6G connectivity, multi-agent coordination, fuzzy adaptive control, and DL framework.