This abstract highlights the essential keywords and clearly describes our project, emphasizing the development of real-time monitoring systems for predictive maintenance. We present an innovative approach using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to predict future demand by integrating historical sales data with real-time social media data on consumer trends and sentiments. Natural Language Processing (NLP) techniques analyze the social media data, which, combined with sales data, forms a comprehensive dataset for training the RNN-LSTM model, capturing complex temporal relationships and improving forecast accuracy. To ensure reliability and robustness, we developed a real-time monitoring system using Prometheus and Grafana, continuously tracking model performance, detecting anomalies, and generating alerts for performance degradation. Automated predictive maintenance mechanisms enable corrective actions such as model redeployment or data cleaning, providing a robust, adaptable solution for demand management in dynamic environments, optimizing performance, ensuring availability, and facilitating data-driven decision-making.