A processor implemented method for extracting natural language elements from an application source code, the method comprising: providing, via one or more hardware interface, the application source code as an input for extracting natural language elements, wherein the application source code comprises executable lines and non-executable lines; extracting, via the one or more hardware interface, commented lines and non-commented lines from the application source code, wherein the commented lines, hard-coded domain values and non-executable sections derived from the non-executable lines constitute the natural language elements in the application source code; splitting, via the one or more hardware interface, the extracted commented lines into code variable declarations (CVDs) and statements with code logic based on usage of the commented lines; grouping, via the one or more hardware interface, the CVDs and CLS as blocks, if they are split into more than one lines, based on start-end keywords or patterns of the CVD and CLS; encoding, via the one or more hardware interface, CVD token sequences and CLS token sequences out of the blocks of CVD and CLS using an input data preparation model; passing, via the one or more hardware interface, the encoded CVD token sequences and CLS token sequences to respective authentic comment classification model to classify a set of authentic comments from a plurality of code comments, wherein the authentic comment classification model for CVD and CLS is pre-generated using a corpus training data, wherein the corpus training data for the authentic comment classification model is generated by generating at least one of syntactical token sequences and non-syntactical token sequences; extracting, via the one or more hardware processors, version and non-version comments out of the set of authentic comments using a version comment classifier model, wherein the version comment classifier model is pre-generated using the corpus training data, wherein the version comments and the non-version comments are extracted as a plurality of comment blocks; and mapping the plurality of comment blocks corresponding to a plurality of executable code blocks present in the application source code, via the one or more hardware interface, using a set of embedded intelligence techniques. providing, via one or more hardware interface, the application source code as an input for extracting natural language elements, wherein the application source code comprises executable lines and non-executable lines; extracting, via the one or more hardware interface, commented lines and non-commented lines from the application source code, wherein the commented lines, hard-coded domain values and non-executable sections derived from the non-executable lines constitute the natural language elements in the application source code; splitting, via the one or more hardware interface, the extracted commented lines into code variable declarations (CVDs) and statements with code logic based on usage of the commented lines; grouping, via the one or more hardware interface, the CVDs and CLS as blocks, if they are split into more than one lines, based on start-end keywords or patterns of the CVD and CLS; encoding, via the one or more hardware interface, CVD token sequences and CLS token sequences out of the blocks of CVD and CLS using an input data preparation model; passing, via the one or more hardware interface, the encoded CVD token sequences and CLS token sequences to respective authentic comment classification model to classify a set of authentic comments from a plurality of code comments, wherein the authentic comment classification model for CVD and CLS is pre-generated using a corpus training data, wherein the corpus training data for the authentic comment classification model is generated by generating at least one of syntactical token sequences and non-syntactical token sequences; extracting, via the one or more hardware processors, version and non-version comments out of the set of authentic comments using a version comment classifier model, wherein the version comment classifier model is pre-generated using the corpus training data, wherein the version comments and the non-version comments are extracted as a plurality of comment blocks; and mapping the plurality of comment blocks corresponding to a plurality of executable code blocks present in the application source code, via the one or more hardware interface, using a set of embedded intelligence techniques.