This paper provides a comprehensive review of the field of concept drift, a phenomenon where the underlying distribution of streaming data changes over time, leading to poor learning outcomes if not addressed. The authors analyze over 130 high-quality publications, focusing on three main components: concept drift detection, understanding, and adaptation. They establish a framework for handling concept drift and discuss popular synthetic and real-world datasets used for evaluation. The paper also covers emerging research directions and highlights new techniques such as active learning under concept drift and fuzzy competence model-based drift detection. The contributions include a clear framework for concept drift research, the introduction of a new component for understanding concept drift, and the identification of several emerging research topics. The paper aims to support researchers in understanding the latest developments and trends in the field of learning under concept drift.This paper provides a comprehensive review of the field of concept drift, a phenomenon where the underlying distribution of streaming data changes over time, leading to poor learning outcomes if not addressed. The authors analyze over 130 high-quality publications, focusing on three main components: concept drift detection, understanding, and adaptation. They establish a framework for handling concept drift and discuss popular synthetic and real-world datasets used for evaluation. The paper also covers emerging research directions and highlights new techniques such as active learning under concept drift and fuzzy competence model-based drift detection. The contributions include a clear framework for concept drift research, the introduction of a new component for understanding concept drift, and the identification of several emerging research topics. The paper aims to support researchers in understanding the latest developments and trends in the field of learning under concept drift.