In this work, we propose a novel, drift-aware aggregation method for ensemble systems that dynamically adapts to changing data conditions while maintaining computational efficiency. Our approach leverages an adaptive error smoothing mechanism, a time-adaptive correlation penalty, and a dynamic mixing parameter to integrate a drift-aware weighting scheme that emphasizes recent performance and preserves model diversity. Comprehensive experiments on real-world datasets demonstrate that our method significantly outperforms traditional aggregation techniques and state-of-the-art algorithms, particularly in environments characterized by recurring concept drift and data scarcity. These results underscore the potential of our approach as a practical and scalable solution for adaptive ensemble aggregation in dynamic, resource-constrained settings.