This review article investigates the convergence of the Internet of Things (IoT) and machine learning (ML) within the SAP ecosystem to facilitate a transition from reactive to intelligent asset management. In the era of Industry 4.0, smart enterprises are increasingly leveraging high-frequency telemetry data from industrial assets to inform the digital core of their ERP systems. The research evaluates the architectural framework provided by the SAP Business Technology Platform, which serves as an orchestration hub for integrating operational technology with information technology. Key machine learning methodologies, including unsupervised anomaly detection and supervised failure prediction models like Random Forests and Gradient Boosting, are examined for their ability to forecast the remaining useful life of critical infrastructure. The study emphasizes the operationalization of these insights through a closed-loop workflow, where predictive signals automatically trigger maintenance notifications and optimize spare parts inventory within SAP S/4HANA. Furthermore, the paper addresses technical constraints such as cybersecurity in the sensor-to-cloud pipeline and the interoperability challenges of legacy brownfield assets. Finally, the research explores future trends in agentic AI and generative troubleshooting assistants, concluding that a fully integrated, data-driven asset strategy is essential for achieving long-term industrial resilience and sustainability goals.