Abstract—Real-time analysis of streaming data is crucial in agricultural environmental monitoring to address quickly changing conditions like seasonal weather changes. Concept drift, where the statistical characteristics of input data evolve, poses a significant problem for static machine learning models. This research presents a drift-aware framework based on a hybrid adaptive windowing method combined with an Online Sequential Extreme Learning Machine (OS-ELM). The strategy involves a multidimensional extension of Adaptive Windowing (ADWIN) that is supplemented by the Kolmogorov–Smirnov statistical test and Hoeffding’s bound to identify and respond to realtime drift. An experimental Internet of Things (IoT) platform was constructed to gather environmental parameters such as temperature, humidity, soil moisture, light, pH, and rainfall. Empirical tests on real and synthetic datasets show that the new framework greatly enhances predictive performance, from 85.86 percent to 97.29 percent when drift handling is activated. The findings emphasize the significance of combining adaptive learning with drift detection for accurate and dependable prediction in precision agriculture.