Regression analysis in data streams presents unique challenges due to the continuous, potentially infinite nature of the data and the phenomenon of concept drift, where the underlying data distribution or the relationship between variables changes over time. Traditional static regression models are ill-equipped to handle such dynamic environments. Adaptive linear filtering techniques offer a powerful paradigm for regression in data streams, allowing models to evolve and adjust to changing patterns. This article explores the application of linear adaptive filtering methods for regression tasks in data stream settings. We discuss the fundamental principles of adaptive filtering, common algorithms like Recursive Least Squares (RLS) and its variants, and their suitability for handling concept drift. By reviewing relevant literature on data stream mining, adaptive learning, and regression techniques, we highlight the advantages of using adaptive linear models, including their computational efficiency, ability to track changing relationships, and theoretical foundations in signal processing. While acknowledging limitations such as sensitivity to parameter choices and potential issues with non-linear relationships, this article argues that linear adaptive filtering provides a robust and efficient foundation for performing regression in dynamic data stream environments, serving as a crucial component in more complex adaptive learning systems.