A method for risk assessment of Parkinson's disease in an individual, the method comprising: extracting Deoxyribonucleic Acid from a sample, wherein the sample has been obtained from a body site of the individual; sequencing the isolated DNA using a sequencer to obtain stretches of bacterial DNA sequences; analyzing, via one or more hardware processors, the stretches of DNA sequences to identify a plurality of bacterial taxa present in the sample, wherein the analysis results in the generation of a bacterial abundance profile having a bacterial abundance value of each of the plurality of bacterial taxa in the sample; pre-processing, via the one or more hardware processors, the bacterial abundance profile to obtain scaled bacterial abundance values of the bacterial abundance profile; evaluating, via the one or more hardware processors, a score for each bacterial taxa of the plurality of bacterial taxa for producing a set of neuroactive compounds, wherein the set of neuroactive compounds are compounds which influences the functioning of a gut-brain axis and wherein the score is evaluated independently for each compound of the set of neuroactive compounds and stored in a bacteria-function matrix; calculating, via the one or more hardware processors, a metabolic potential corresponding to each compound of the set of neuroactive compounds using the bacteria function matrix and the scaled bacterial abundance values, wherein the metabolic potential is indicative of the capability of the bacterial community for producing the neuroactive compound; generating, via the one or more hardware processors, a binary classification model utilizing the metabolic potential of each compound of the set of neuroactive compounds using machine learning techniques; predicting, via the one or more hardware processors, the risk of the individual to develop or suffering from Parkinson's disease in a risk or no risk, using the binary classification model based on a predefined set of conditions; and designing therapeutic approaches, through targeting the bacterial groups that are capable of producing a set of neurotoxic compounds or facilitating growth of healthy microbes, wherein the set of neurotoxic compounds are compounds which negatively affects the functioning of the gut-brain axis. extracting Deoxyribonucleic Acid from a sample, wherein the sample has been obtained from a body site of the individual; sequencing the isolated DNA using a sequencer to obtain stretches of bacterial DNA sequences; analyzing, via one or more hardware processors, the stretches of DNA sequences to identify a plurality of bacterial taxa present in the sample, wherein the analysis results in the generation of a bacterial abundance profile having a bacterial abundance value of each of the plurality of bacterial taxa in the sample; pre-processing, via the one or more hardware processors, the bacterial abundance profile to obtain scaled bacterial abundance values of the bacterial abundance profile; evaluating, via the one or more hardware processors, a score for each bacterial taxa of the plurality of bacterial taxa for producing a set of neuroactive compounds, wherein the set of neuroactive compounds are compounds which influences the functioning of a gut-brain axis and wherein the score is evaluated independently for each compound of the set of neuroactive compounds and stored in a bacteria-function matrix; calculating, via the one or more hardware processors, a metabolic potential corresponding to each compound of the set of neuroactive compounds using the bacteria function matrix and the scaled bacterial abundance values, wherein the metabolic potential is indicative of the capability of the bacterial community for producing the neuroactive compound; generating, via the one or more hardware processors, a binary classification model utilizing the metabolic potential of each compound of the set of neuroactive compounds using machine learning techniques; predicting, via the one or more hardware processors, the risk of the individual to develop or suffering from Parkinson's disease in a risk or no risk, using the binary classification model based on a predefined set of conditions; and designing therapeutic approaches, through targeting the bacterial groups that are capable of producing a set of neurotoxic compounds or facilitating growth of healthy microbes, wherein the set of neurotoxic compounds are compounds which negatively affects the functioning of the gut-brain axis.