A method for identification of key driver responsible for bringing a change in a microbial population, the method comprising a processor implemented steps of: getting a sample from a first set of individuals and a second set of individuals; extracting DNA samples from the sample from the first and the second set of individuals; sequencing each of the DNA samples using a sequencer to generate a plurality of DNA sequences; filtering and processing the plurality of DNA sequences for removing the low quality DNA sequences and non-essential DNA fragments; creating two matrices of microbial abundance profile of the plurality of DNA sequences corresponding to the first set and the second set of individuals, wherein each matrix of microbial abundance profile includes abundance of microbial organisms corresponding to each member belonging to the microbial population; normalizing each matrix using a normalization method, wherein the normalization method is a cumulative sum scaling method; representing microbial organisms in each matrix as a plurality of nodes; generating a microbial association network for the first set and the second set using the normalized matrices; computing Jaccard node index and Jaccard edge index between the microbial association network of first set and the microbial association network of the second set; identifying if the Jaccard node index is higher than a first predefined value and the Jaccard edge index is lower than a second predefined value, wherein the first predefined value is any value between 0.6 and 1 and the second predefined value is any value between 0 and 0.6 confirming a high rewiring between the two sets with minimal addition or removal of another node; quantifying the network rewiring if the Jaccard node index is higher than the first predefined value and the Jaccard edge index is lower than the second predefined value; computing a scaled change in betweenness for each of the nodes in the microbial association network of the second set with respect to the microbial association network of the first set; calculating a neighbor shift score for each of the nodes in the microbial association network of the second set with respect to the microbial association network of the first set using a predefined formula and a Netshift methodology, wherein the Netshift methodology calculates a Neighbor Shift (NESH) to quantify changes in association of each of the nodes to distinguish between a healthy state and a diseased state; calculating a statistical significance value for the neighbor shift score for each of the nodes; and identifying a node as the key driver if the statistical significance value of its neighbor shift score is less than a third predefined value and the scaled change in betweenness is positive, wherein the third predefined value is less than or equal to 0.1. getting a sample from a first set of individuals and a second set of individuals; extracting DNA samples from the sample from the first and the second set of individuals; sequencing each of the DNA samples using a sequencer to generate a plurality of DNA sequences; filtering and processing the plurality of DNA sequences for removing the low quality DNA sequences and non-essential DNA fragments; creating two matrices of microbial abundance profile of the plurality of DNA sequences corresponding to the first set and the second set of individuals, wherein each matrix of microbial abundance profile includes abundance of microbial organisms corresponding to each member belonging to the microbial population; normalizing each matrix using a normalization method, wherein the normalization method is a cumulative sum scaling method; representing microbial organisms in each matrix as a plurality of nodes; generating a microbial association network for the first set and the second set using the normalized matrices; computing Jaccard node index and Jaccard edge index between the microbial association network of first set and the microbial association network of the second set; identifying if the Jaccard node index is higher than a first predefined value and the Jaccard edge index is lower than a second predefined value, wherein the first predefined value is any value between 0.6 and 1 and the second predefined value is any value between 0 and 0.6 confirming a high rewiring between the two sets with minimal addition or removal of another node; quantifying the network rewiring if the Jaccard node index is higher than the first predefined value and the Jaccard edge index is lower than the second predefined value; computing a scaled change in betweenness for each of the nodes in the microbial association network of the second set with respect to the microbial association network of the first set; calculating a neighbor shift score for each of the nodes in the microbial association network of the second set with respect to the microbial association network of the first set using a predefined formula and a Netshift methodology, wherein the Netshift methodology calculates a Neighbor Shift (NESH) to quantify changes in association of each of the nodes to distinguish between a healthy state and a diseased state; calculating a statistical significance value for the neighbor shift score for each of the nodes; and identifying a node as the key driver if the statistical significance value of its neighbor shift score is less than a third predefined value and the scaled change in betweenness is positive, wherein the third predefined value is less than or equal to 0.1.