Machine learning models deployed in data streaming environments often suffer from concept drift, where the underlying data distribution changes over time, leading to performance degradation. Detecting and adapting to these shifts in real time is crucial to maintaining model accuracy and reliability. This study introduces the Sliding Adaptive Beta Distribution Model (SABeDM), a novel approach for concept drift detection and adaptation in dynamic data streams.