Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of infrastructure. Traditional SHM methods rely on periodic inspections and centralized data processing, leading to delays and inefficiencies. This chapter proposes an integrated framework combining the Internet of Things (IoT), finite element analysis (FEA), and edge machine learning (ML) to enable real-time, data-driven decision-making for SHM. IoT-based sensor networks continuously collect vibration, strain, and environmental data, which are processed using FEA models for predictive analysis. Edge ML algorithms optimize operational efficiency by executing anomaly detection routines for predictive maintenance alongside real-time applications at local edge nodes to minimize processing delays and decrease cloud dependency. Recorded simulation runs and case-based validation show that the proposed method achieves better structural anomaly detection precision along with faster response times. This framework uses IoT FEA and edge ML technology to create a scalable solution for continuous SHM that enhances both safety and maintenance scheduling for critical infrastructure while maintaining cost-effectiveness.