In industrial settings, ensuring the uninterrupted operation of induced-draft fans is essential for maintaining profitability. However, relying solely on routine maintenance schedules can overlook potential failures and incur unnecessary expenses. Additionally, visualizing maintenance trends, particularly concerning fan vibration, becomes challenging due to dust scale build-up. This initiative focuses on implementing predictive maintenance strategies tailored to induced-draft fans. By harnessing advanced analytics and real-time data, we aim to anticipate potential failures and optimize maintenance schedules proactively. This approach enhances operational reliability, minimizes downtime, and maximizes cost-effectiveness in fan operations.