Existing approaches for item matching that are used for retail strategies are based on similarity matching, however, do not consider user mindset, magnitude present across quantitative AVs and segment specific customer interest on certain qualitative AVs. Embodiments of the present disclosure provide a method and system for Machine Learning (ML) based item matching by considering user mindset, magnitude present across quantitative AVs and segment specific customer interest on certain qualitative AV. The item matching approach disclosed, performs data analytics at the AV level to identify possible close matching items from the list of available partially matching as well as non-matching items. The method disclosed primarily performs Attribute (AT) enrichment by quantizing all the qualitative AVs to be analyzed. Weights are assigned to all the quantized AVs based on a Demand Transfer (DT) value provided by a Customer Decision Tree (CDT), wherein the CDT captures the user mindset.