Enhanced Temperature Management in Pharmaceutical Manufacturing with IoT and Extreme Gradient Boosting

Ensuring the quality and performance of pharmaceutical solutions depends primarily on temperature control through manufacturing. Inaccurate temperature control is a common problem with traditional techniques, which may affect product quality. This research proposes an improved temperature management system that combines Internet of Things (IoT) technology with Extreme Gradient Boosting (XGBoost) to improve temperature control in pharmaceutical production. In a centralized system, data is sent in real-time with IoT sensors that continually monitor temperature throughout production. This data is processed using XGBoost, a robust gradient boosting algorithm to optimize control methods and predict temperature variations. Improved temperature stability, less product degradation risk, and compliance with industry standards are the goals of the proposed system, which uses the IoT for real-time monitoring and XGBoost for predictive analytics. This integrated method is beneficial in pharmaceutical production, as experimental data shows considerable gains in temperature precision and consistency.