Industrial operations increasingly rely on Internet of Things (IoT) sensors to monitor machine health, process variables, and environmental conditions. This paper presents an end-to-end approach for deploying IoT-driven predictive maintenance dashboards that transform raw sensor streams into actionable maintenance decisions. We describe a scalable data architecture for real-time ingestion, processing, and storage; predictive models for remaining useful life (RUL) estimation and anomaly detection; a health-score framework that synthesizes multiple indicators; and a dashboard design that supports operators, maintenance planners, and line managers. A pilot deployment in a manufacturing setting demonstrates measurable improvements in asset uptime, reduced mean time to repair (MTTR), and more efficient maintenance scheduling. Key contributions include [1] an integrated IoT-to-dashboard framework bridging data science and operations, [2] a modular modeling approach combining time-series forecasting and anomaly detection with interpretable health scores, [3] a dashboard design guided by human factors and decision-support needs, and [4] practical guidelines for data governance, security, and deployment. The results indicate that well-designed predictive dashboards can shorten decision cycles, increase asset availability, and reduce maintenance costs while maintaining data quality and security.