Impurity Reduction is a fundamental concept in Decision tree learning, used to measure how well a Feature splits the data into Distinct classes. It is achieved by calculating metrics such as Gini Impurity or entropy before and after a split to determine the Effectiveness of the feature in Separating different classes.