CNC machine rooms are industrial work environments with high levels of noise and potential air pollution originating from high-speed spindle motor operations, mechanical friction, and metal-cutting processes. Currently, monitoring these conditions is largely done manually. This study aims to design and implement an Internet of Things (IoT)-based system that can continuously and in real time monitor the work environment for noise and air pollution. This study employed a research and development approach integrating noise and air quality sensors controlled by an ESP32 microcontroller and connected to a cloud platform for data acquisition and visualization. The system's accuracy and performance were evaluated by taking direct measurements of various CNC router cutting materials and comparing the sensor results with those of standard measuring instruments. Results showed that the system reliably monitored noise and dust concentration, with average measurement errors of 2.9% and 1.3%, respectively. Implementing this system
effectively provides an early warning when workplace parameters exceed safety thresholds, thereby supporting rapid, data-driven occupational safety and health (OSH) risk mitigation decisions.