Concept drift is a significant challenge in machine learning, characterized by changes in the data distribution that can degrade model performance over time. This phenomenon occurs when the relationship between input features and target variables shifts, often due to unforeseen events like pandemics or gradual changes in user behavior. To address concept drift, continuous monitoring and adaptive strategies are essential, including methods like online learning, periodic re-training, and ensemble learning. These approaches help models remain accurate and relevant by accommodating new data patterns and preventing model decay. Differentiating between concept drift and other data shifts, such as covariate and data drift, is crucial for implementing effective corrective measures. Although there is no universal solution for concept drift, ongoing research and various methodologies provide a foundation for building systems that detect and adapt to these changes, ensuring robust and reliable machine learning models.