In real-world streaming data from sensor networks, this drift occurs because of changes in the environment they are deployed in or in the reliability of the sensors used. To solve the problem of prediction in the dynamic stream, this study develops a Drift Detection Method based on PCA with a hybrid ensemble classifier. PCA is used for monitoring feature distributions in real time to detect deviations from the reconstruction which may signify shifts in the underlying data distributions. In case drift occurs, the hybrid ensemble classifier is capable of changing classifier weights and employing classifiers that are most suitable for the current data characteristics making the devised technique highly relevant and accurate. The performance of the proposed model is evaluated using an air quality sensor dataset and IoTID20 datasets where ensemble classifier selection thresholds are allowed to learn adaptively concerning the concept drift. The model shows improved drift handling in the use of PCA for detection and the hybrid ensemble for selective and accurate. The accuracy for the IoTID20 dataset was 94.23%, and for the air quality dataset, it was 97.6%. Experimental results show that improved accuracy in handling drift enhances the model's application to sensor-based systems with non-stationary data.