<p>In pharmaceutical manufacturing, maintaining equipment used in aseptic processing is essential to ensure sterility, product integrity, and regulatory compliance. Vial washers, which form the backbone of sterile fill-finish operations, are highly sensitive to operational disruptions, making predictive maintenance vital for minimizing downtime and avoiding batch failures. Despite growing interest in predictive maintenance, limited research has addressed AI applications specifically tailored to vial processing equipment in pharmaceutical settings. This study develops and evaluates a deep learning model using Long Short-Term Memory (LSTM) networks for real-time predictive maintenance of vial washers. Sensor data—including vibration, temperature, and acoustic signals—is collected from rotating components to generate multivariate time-series input. The LSTM model is selected due to its proven ability to capture long-range temporal dependencies in sequential data, making it well-suited for identifying early-stage equipment degradation. Traditional algorithms like Random Forest and XGBoost were initially considered but not implemented due to their inability to model sequential dependencies without intensive feature engineering. Preliminary results from simulated datasets indicate high accuracy in predicting wear-related faults, with lead times of up to 10 to 12&#xa0;h prior to failure events. The system architecture is designed to be scalable and compatible with GxP compliance standards. This research presents a scalable, AI-powered framework to proactively manage equipment maintenance in sterile pharmaceutical environments, potentially reducing unplanned downtime and improving compliance readiness. The current evaluation was performed in a simulated/replayed setting using a pump-motor proxy for vial washer drives. 1. Keywords. Predictive Maintenance, Vial Washer, Pharmaceutical Manufacturing, Sensor Fusion, Aseptic Processing, Condition-Based Monitoring, Time-Series Forecasting.</p>