Statistical techniques for drift detection are essential tools in machine learning and data science, used to identify changes in data distributions over time. Data drift can significantly impact model performance, leading to inaccurate predictions if not addressed. Various statistical methods, such as the Kolmogorov-Smirnov (KS) test, Population Stability Index (PSI), and Kullback-Leibler divergence, are employed to monitor and quantify these changes. By applying these techniques, practitioners can ensure that their models remain robust and reliable, adapting to evolving data patterns and maintaining their effectiveness in real-world applications. Understanding these methods is crucial for effective model management.