This study proposes an advanced alarm recommendation system tailored for a multilayer ceramic capacitor (MLCC) production line, harnessing the power of natural language processing (NLP) and Case-Based Reasoning (CBR) to enhance operational efficiency and minimize downtime. The primary goal is to provide relevant, prioritized alarm recommendations to operators, addressing the common issue of notification overload in traditional alarm systems, which often leads to user fatigue and critical alerts being overlooked. These oversights can cause significant operational disruptions. By leveraging NLP techniques, the system analyzes alarm descriptions and historical data, identifying semantic patterns and relationships. This allows it to understand the severity and context of each alarm, ensuring that operators focus on the most critical issues. In doing so, unnecessary distractions are reduced, safety is improved, and overall productivity on the production line is enhanced. Additionally, the system offers contextualized recommendations by correlating alarms with key machine parameters, providing valuable insights into specific actions required to efficiently resolve the issue. Moreover, CBR is integrated into the system to improve predictive maintenance processes. By analyzing past cases of equipment failures and repair actions, the system can offer optimized solutions for current maintenance needs, ultimately reducing downtime and preventing costly breakdowns. This approach enables operators to act proactively, addressing potential problems before they escalate into major failures. The combination of NLP and CBR allows for a dynamic and intelligent response to fluctuating production conditions, and personalized recommendations tailored to operators’ past interactions, preferences, and experience levels. This personalized approach further enhances decision-making, resulting in faster response times and more efficient workflows. In summary, this alarm recommendation system not only tackles the issue of alarm fatigue but also introduces a more intelligent, context-aware, and personalized approach to managing alarms. The integration of CBR for predictive maintenance represents a significant advancement in ensuring the smooth operation of MLCC production lines, reducing downtime, optimizing human-machine interaction, and improving long-term production efficiency. In future research, it will be possible that through hyperparameter optimization using the system’s recurrent neural network (RNN) algorithms, it will achieve a higher accuracy rate than the current one of around 86% to prevent fatal accidents in this type of factories in Industry 5.0.