Explainable AI is vital for modern machine learning, with counterfactual explanations offering promising instance-level insights. Existing methods often rely on synthetic examples, which can be biased due to simplified assumptions, imperfect models, or skewed data distributions. These approaches also assume access to the entire dataset, which is impractical in streaming scenarios where data arrives continuously. Streaming explanations provide timely, up-to-date insights, handling large-scale data efficiently without storing the entire dataset. To address these challenges, we propose a scalable algorithm for selecting real counterfactual examples, ensuring diversity and relevance. Our approach works in streaming settings with \(O(\log k)\) complexity per item and guarantees quality. Experiments on real and synthetic datasets show it outperforms baselines and remains robust under adversarial conditions.