A processor implemented method for assessment of cognitive load from bio-potentials, the method comprising: acquiring, via a multichannel wearable endosomatic electrodermal activity device by one or more hardware processors, a plurality of bio-potential signals providing a task specific data of a subject performing a cognitive task, wherein the plurality of bio-potential signals are time stamped; extracting a baseline data and a task data based on time stamps associated with each of the plurality of bio-potential signals, by the one or more hardware processors, wherein the baseline data corresponds to segment of each of the plurality of bio-potential signals when the subject experiences a nil cognitive load and the task data corresponds to segment of each of the plurality of bio-potential signals when the subject experiences an active cognitive load during the cognitive task being performed; deriving, by the one or more hardware processors, a plurality of deviation signals from the plurality bio-potential signals by: computing mean of each of the plurality of bio-potential signals comprising the base-line data and the task data; and subtracting each of the base-line data and the task data from the mean of each of the plurality of bio-potential signals; extracting, by the one or more hardware processors, a plurality of features from the plurality of deviation signals, wherein the plurality of features comprising a statistical set of features and a spectral set of features, wherein the set of statistical features comprises computing: mean, median, standard deviation, skewness, kurtosis, minimum levels and maximum levels of the plurality of bio-potential signals using the task data; and normalized kurtosis and normalized variance values with respect to the baseline data and the task data; and wherein the set of spectral features comprises a mean frequency, power at the mean frequency, total power, mean power, median frequency, power at median frequency, peak frequency, peak power, signal band-power, first moment, second moment, and third moment; determining, by the one or more hardware processors, a recommended set of features from the plurality of features using a Feature Discovery Platform, wherein the FDP extracts signal property based generic features (SPGF) and determines the recommended set of features in accordance with the cognitive task; and training a classifier for classifying the cognitive task into one of a high load task and a low load task using the recommended set of features, by the one or more hardware processors. acquiring, via a multichannel wearable endosomatic electrodermal activity device by one or more hardware processors, a plurality of bio-potential signals providing a task specific data of a subject performing a cognitive task, wherein the plurality of bio-potential signals are time stamped; extracting a baseline data and a task data based on time stamps associated with each of the plurality of bio-potential signals, by the one or more hardware processors, wherein the baseline data corresponds to segment of each of the plurality of bio-potential signals when the subject experiences a nil cognitive load and the task data corresponds to segment of each of the plurality of bio-potential signals when the subject experiences an active cognitive load during the cognitive task being performed; deriving, by the one or more hardware processors, a plurality of deviation signals from the plurality bio-potential signals by: computing mean of each of the plurality of bio-potential signals comprising the base-line data and the task data; and subtracting each of the base-line data and the task data from the mean of each of the plurality of bio-potential signals; computing mean of each of the plurality of bio-potential signals comprising the base-line data and the task data; and subtracting each of the base-line data and the task data from the mean of each of the plurality of bio-potential signals; extracting, by the one or more hardware processors, a plurality of features from the plurality of deviation signals, wherein the plurality of features comprising a statistical set of features and a spectral set of features, wherein the set of statistical features comprises computing: mean, median, standard deviation, skewness, kurtosis, minimum levels and maximum levels of the plurality of bio-potential signals using the task data; and normalized kurtosis and normalized variance values with respect to the baseline data and the task data; and mean, median, standard deviation, skewness, kurtosis, minimum levels and maximum levels of the plurality of bio-potential signals using the task data; and normalized kurtosis and normalized variance values with respect to the baseline data and the task data; and wherein the set of spectral features comprises a mean frequency, power at the mean frequency, total power, mean power, median frequency, power at median frequency, peak frequency, peak power, signal band-power, first moment, second moment, and third moment; determining, by the one or more hardware processors, a recommended set of features from the plurality of features using a Feature Discovery Platform, wherein the FDP extracts signal property based generic features (SPGF) and determines the recommended set of features in accordance with the cognitive task; and training a classifier for classifying the cognitive task into one of a high load task and a low load task using the recommended set of features, by the one or more hardware processors.