Data distributions often change over time in non-stationary environments, rendering models trained on previous data invalid in deployed environments. To prevent a decline in predictive performance, machine learning models must monitor the incoming data stream to capture changes in data distributions, known as Concept Drift (CD). This study extends a probabilistic real-drift detection method by adopting a new strategy to detect the CD and adapt the underlying model to learn new concepts accordingly. Detecting such drift is critical, allowing for timely model updates based on new data, thereby mitigating potential prediction errors. Additionally, we refine the detector’s thresholds to better distinguish between drifting and non-drifting samples. Using established stream generators, we create diverse synthetic datasets to compare the performance of our enhanced method with state-of-the-art drift detection techniques under both abrupt and gradual drift scenarios. This broad range of drift scenarios allows for thorough assessment. The proposed approach demonstrates high performance across different base learners and varied data streams and often outperforms other methods.