Abstract: A method and system for generating Customer Decision Tree (CDT) for an entity in accordance with an attribute value (AV) based demand transfer estimation for a product category using machine learning, is disclosed. The method includes aggregating very high volume of data associated with a plurality of AVs of a product category at a plurality of aggregation levels. Further, generating a data matrix, which represents data is a structured format for machine learning, at a predefined aggregation level for the product category and generating a prediction model with the data matrix to determine predicted AV sales for each AV at the predefined aggregation level. Further, optimizing the trained prediction model. Thereafter, generate the CDT utilizing the optimized prediction model, a Demand Transfer (DT) estimator, a scenario generator and a hierarchy generator. Machine learning based DT is more accurate, effectively generating more accurate CDT tree.