<p>The advent of Industry 4.0, marked by the integration of cyber-physical systems and advanced sensing technologies, has revolutionized the manufacturing sector. A significant challenge within this context is effectively utilizing vast data streams from production processes to enable real-time adaptive control. This research addresses this challenge by reformulating the assembly line balancing problem (ALBP) through multi-agent reinforcement learning (MARL) and aligning assembly line rebalancing (ALR) with maintenance decision-making to enhance adaptability, efficiency, and responsiveness. Traditional ALBP methodologies typically assume stable workstation capabilities, often neglecting machine degradation and unforeseen failures. This study incorporates these factors within mixed model assembly lines (MMALs). The developed data-driven model integrates assembly line rebalancing, fault prediction, and real-time system control, utilizing stochastic modeling and deep learning techniques. Offline training with simulation data ensures robustness, while online implementation allows real-time data to continuously inform the system, enabling autonomous decisions that optimize operational efficiency. By incorporating proactive maintenance strategies, the model enhances the reliability of the entire manufacturing system. Results demonstrate significant reductions in downtime and maintenance costs, alongside improved cycle times and operational efficiency. This research highlights the flexible and reconfigurable nature of automated assembly lines, particularly within MMALs, addressing the multifaceted challenges of real-time manufacturing environments. This study bridges theoretical advancements with practical implementations, providing a robust and adaptable solution for assembly line management and marking a significant advancement in manufacturing process optimization.</p>