A processor implemented method for product data categorization, the method further comprising: acquiring, via one or more hardware processors, an input describing a set of product data from an application data store for categorization, characterized in that: preprocessing, via the one or more hardware processors, the set of product data by removing extraneous or unwanted text based on a predefined template, wherein the extraneous text includes comma, dots, error code, dropping unnecessary columns, unmatched product data text from the predefined template; creating, via the one or more hardware processors, a dictionary for the set of product data based on a set of attributes further comprising a product key with its corresponding product value, wherein a dictionary data is built with a product category and products as a key-value pair; extracting, via the one or more hardware processors, a multi-level contextual data for the set of product data, by assigning a weight to each product data based on likelihood of the product for arriving at a suitable relevancy or probability score, and creating a set of datapoints for each product data, wherein the weight adds to sharpness or steepness of the input, which leverages semantic similarity and iteration of the weights across each product data depicts probability scenario of contextual data understanding and weighted data represents contextuality; and categorizing, via the one or more hardware processors, the set of product data by feeding the set of data points to compute a minimum count, a total size, total number of epochs, a skip gram value and a hierarchical softmax upon pivoting the set of product data to obtain counts based on the product category and reindexing the assigned weights to align with a pivot table index followed by creating the set of data points based on the assigned weights using the pivot table index for each product categorization, wherein contextually aware datapoints are created through dictionary and weight assignment are passed based on a pretrained Word2Vec model. preprocessing, via the one or more hardware processors, the set of product data by removing extraneous or unwanted text based on a predefined template, wherein the extraneous text includes comma, dots, error code, dropping unnecessary columns, unmatched product data text from the predefined template; creating, via the one or more hardware processors, a dictionary for the set of product data based on a set of attributes further comprising a product key with its corresponding product value, wherein a dictionary data is built with a product category and products as a key-value pair; extracting, via the one or more hardware processors, a multi-level contextual data for the set of product data, by assigning a weight to each product data based on likelihood of the product for arriving at a suitable relevancy or probability score, and creating a set of datapoints for each product data, wherein the weight adds to sharpness or steepness of the input, which leverages semantic similarity and iteration of the weights across each product data depicts probability scenario of contextual data understanding and weighted data represents contextuality; and categorizing, via the one or more hardware processors, the set of product data by feeding the set of data points to compute a minimum count, a total size, total number of epochs, a skip gram value and a hierarchical softmax upon pivoting the set of product data to obtain counts based on the product category and reindexing the assigned weights to align with a pivot table index followed by creating the set of data points based on the assigned weights using the pivot table index for each product categorization, wherein contextually aware datapoints are created through dictionary and weight assignment are passed based on a pretrained Word2Vec model.