We introduce Dynamic Drift-Adaptive Ensemble-based Quality of Transmission (QoT) Classification Framework (DAEQoT) to effectively verify the feasibility of a lightpath while maintaining high prediction accuracy in a dynamic Optical Transport Network (OTN) scenario. This framework adopts the Early Drift Detection Method (EDDM) to identify any significant increase in the prediction error. Moreover, Ensemble Learning is implemented to enhance the accuracy of the Master QoT model by combining other Supportive QoT classifiers when an early drift warning is reported. Thus, prediction error and complete model retraining are mitigated. DAEQoT achieves the best accuracy of 98.56% and reduces the execution time up to 52.81% compared to state-of-the-art offline and online machine learning approaches.