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Stream mining, especially with concept drift, presents significant challenges across various domains. As data streams evolve over time, initial models become less effective. We present a novel approach using fuzzy ARTMAP's adaptability and neural networks' robustness to address concept drift. Our method dynamically updates models based on changing data distributions, enabling real-time adap- tation. By integrating fuzzy ARTMAP with backpropagation, it facilitates agile learning and accurate predictions in evolving scenarios. Through rigorous exper- iments, we demonstrate the effectiveness of our method in managing concept drift and achieving substantial performance improvements. The achieved accu- racy of 85.07% and F1 score of 72.47 demonstrate the effectiveness of the approach in real-time classification tasks. This research extends beyond just performance metrics. By leveraging the interpretability of fuzzy ARTMAP, we gain valuable insights into the mechanisms that enable our model to adapt to concept drift. This deeper understanding paves the way for further advancements in this area. [ABSTRACT FROM AUTHOR]