A processor implemented method for automated classification of a plurality of variables, the method comprising: receiving, via a user interface, an application source code as an input; parsing, via one or more hardware processors, the application source code to extract the plurality of variables present in the application source code, wherein the plurality of variables have a plurality of properties and relations; eliminating, via the one or more hardware processors, one or more intermediate transient variables out of the plurality of variables, wherein the application source code is used and extracts the properties and relationships of the plurality of variables and wherein a variable filter is applied and the one or more intermediate transient variables are applied; generating, via the one or more hardware processors, a plurality of vectors using the plurality of variables based on data element properties and relational weightage, wherein the plurality of vectors are encoded based on at least one of a relation analysis and an analysis based on variable properties; applying, via the one or more hardware processors, an unsupervised distribution agnostic clustering, DAC, technique on the plurality of vectors based on a cluster size provided by a user, wherein the DAC results in generation of a set of sparse relational clusters and a first set of output clusters, wherein the unsupervised DAC depends on a maximum cluster size threshold as an input and the DAC technique is implemented by automatically estimating the number of clusters for each recursion and partitioning space into the first set of output clusters; applying, via the one or more hardware processors, a second unsupervised DAC technique based on properties for the set of sparse relational clusters to generate property based output clusters and append the property based output clusters to the first set and derive a final set of output clusters, wherein the second unsupervised DAC technique is applied when there is availability of sparse relational variables and wherein the second unsupervised DAC technique is performed by combining relational and property information into the plurality of vectors to recursively cluster and obtain results; and applying, via the one or more hardware processors, a cluster naming algorithm on the final set of output clusters to name the each of the final set of clusters of the plurality of variables, wherein the cluster naming algorithm is performed based on at least one of centroid analysis, recurring sub term and deterministic techniques. receiving, via a user interface, an application source code as an input; parsing, via one or more hardware processors, the application source code to extract the plurality of variables present in the application source code, wherein the plurality of variables have a plurality of properties and relations; eliminating, via the one or more hardware processors, one or more intermediate transient variables out of the plurality of variables, wherein the application source code is used and extracts the properties and relationships of the plurality of variables and wherein a variable filter is applied and the one or more intermediate transient variables are applied; generating, via the one or more hardware processors, a plurality of vectors using the plurality of variables based on data element properties and relational weightage, wherein the plurality of vectors are encoded based on at least one of a relation analysis and an analysis based on variable properties; applying, via the one or more hardware processors, an unsupervised distribution agnostic clustering, DAC, technique on the plurality of vectors based on a cluster size provided by a user, wherein the DAC results in generation of a set of sparse relational clusters and a first set of output clusters, wherein the unsupervised DAC depends on a maximum cluster size threshold as an input and the DAC technique is implemented by automatically estimating the number of clusters for each recursion and partitioning space into the first set of output clusters; applying, via the one or more hardware processors, a second unsupervised DAC technique based on properties for the set of sparse relational clusters to generate property based output clusters and append the property based output clusters to the first set and derive a final set of output clusters, wherein the second unsupervised DAC technique is applied when there is availability of sparse relational variables and wherein the second unsupervised DAC technique is performed by combining relational and property information into the plurality of vectors to recursively cluster and obtain results; and applying, via the one or more hardware processors, a cluster naming algorithm on the final set of output clusters to name the each of the final set of clusters of the plurality of variables, wherein the cluster naming algorithm is performed based on at least one of centroid analysis, recurring sub term and deterministic techniques.