A processor implemented method comprising: receiving, via one or more hardware processors of a bioreactor, a cell culture medium of one or more human stem cells and a sparger supply; obtaining, via the one or more hardware processors, by using a plurality of soft sensors placed at one or more regions of the bioreactor, a soft sensor data pertaining to a first set of parameters and a second set of parameters from the cell culture medium, wherein the first set of parameters comprises an inoculum density, a cell aggregate size, a pH value, a nutrient concentration, a temperature, a by-product concentration, an agitation rate, a sparging rate of the sparger supply, a macro-sparger flow rate, and a feed rate, wherein the second set of parameters comprises a base-addition controller, and wherein the second set of parameters further comprises at least a subset of the first set of parameters; pre-processing, via the one or more hardware processors, the soft sensor data to obtain a plurality of pre-processed sensor values; performing simulation, via the one or more hardware processors, by using a physics based mathematical model, on the plurality of pre-processed sensor values pertaining to the first set of parameters, and a third set of parameters to obtain a set of simulated variables, wherein the third set of parameters are obtained from a domain knowledge database; iteratively optimizing, via the one or more hardware processors, the second set of parameters using the set of simulated variables to obtain a set of optimized real-time operating variables, until a value of a cell density reaches a corresponding predefined threshold, wherein the set of optimized real-time operating variables are obtained to evaluate the cell density using one or more associated constraint values; controlling, via the one or more hardware processors, a variation in a pH value, and a cell aggregate size using one or more control strategies based on the set of simulated variables for the growth of the one or more human stem cells; performing, via the one or more hardware processors, a comparison of the set of optimized real-time operating variables and the second set of parameters to obtain a set of correction factors; sending, via the one or more hardware processors, a plurality of recommended parameter values to a plant automation system based on the set of correction factors; and updating, via the one or more hardware processors, the second set of parameters based on the plurality of recommended parameter values. receiving, via one or more hardware processors of a bioreactor, a cell culture medium of one or more human stem cells and a sparger supply; obtaining, via the one or more hardware processors, by using a plurality of soft sensors placed at one or more regions of the bioreactor, a soft sensor data pertaining to a first set of parameters and a second set of parameters from the cell culture medium, wherein the first set of parameters comprises an inoculum density, a cell aggregate size, a pH value, a nutrient concentration, a temperature, a by-product concentration, an agitation rate, a sparging rate of the sparger supply, a macro-sparger flow rate, and a feed rate, wherein the second set of parameters comprises a base-addition controller, and wherein the second set of parameters further comprises at least a subset of the first set of parameters; pre-processing, via the one or more hardware processors, the soft sensor data to obtain a plurality of pre-processed sensor values; performing simulation, via the one or more hardware processors, by using a physics based mathematical model, on the plurality of pre-processed sensor values pertaining to the first set of parameters, and a third set of parameters to obtain a set of simulated variables, wherein the third set of parameters are obtained from a domain knowledge database; iteratively optimizing, via the one or more hardware processors, the second set of parameters using the set of simulated variables to obtain a set of optimized real-time operating variables, until a value of a cell density reaches a corresponding predefined threshold, wherein the set of optimized real-time operating variables are obtained to evaluate the cell density using one or more associated constraint values; controlling, via the one or more hardware processors, a variation in a pH value, and a cell aggregate size using one or more control strategies based on the set of simulated variables for the growth of the one or more human stem cells; performing, via the one or more hardware processors, a comparison of the set of optimized real-time operating variables and the second set of parameters to obtain a set of correction factors; sending, via the one or more hardware processors, a plurality of recommended parameter values to a plant automation system based on the set of correction factors; and updating, via the one or more hardware processors, the second set of parameters based on the plurality of recommended parameter values.