This project proposes a novel approach to bolster workplace safety in foundries through the implementation of an integrated sensor network for real-time air quality monitoring and risk prediction. Foundry environments are notorious for their hazardous conditions, primarily due to the emission of harmful particulate matter and gases during metal casting processes. Traditional safety measures often fall short of effectively mitigating these risks. Therefore, our system aims to revolutionize safety protocols by providing continuous, accurate monitoring of air quality parameters such as particulate matter concentration, gas emissions, temperature, and humidity. Leveraging advanced data analytics and machine learning algorithms, the system can predict potential risk events and provide timely alerts to workers and management, enabling proactive intervention strategies. By establishing a safer work environment, this innovative solution not only safeguards the health and well-being of foundry workers but also enhances operational efficiency and productivity. This interdisciplinary endeavor amalgamates expertise in sensor technology, data science, and industrial safety to forge a pathway toward sustainable workplace practices in the foundry industry.