A processor-implemented method of adaptive learning of models for a manufacturing system, comprising: collecting a plurality of measured ground truth profiles and operating data as input data, via one or more hardware processors, from one or more databases at a pre-determined frequency, wherein each of the plurality of the measured ground truth profiles comprises information on one or more manufacturing system parameters and one or more variables corresponding to each of the one or more manufacturing system parameters; characterized by the following steps: pre-processing the input data, via the one or more hardware processors, wherein the pre-processing of the input data comprises conditioning the input data to satisfy one or more pre-defined data quality requirements; obtaining, via the one or more hardware processors, simulated data based on the pre-processed data using at least one soft sensor, wherein the at least one soft-sensor comprises a physics-based soft sensor and a data-driven soft sensor, wherein the simulated data is integrated with pre-processed data to obtain integrated data; determining a Profile Deviation Index value for each of the plurality of the measured ground truth profiles in the integrated data, via the one or more hardware processors, wherein the PDI value represents a quantified distortion in each of the plurality of the measured ground truth profiles; comparing the PDI value of each of the plurality of the measured ground truth profiles with a threshold of distortion, via the one or more hardware processors, wherein the PDI value exceeding the threshold of distortion for any of the plurality of the measured ground truth profiles indicates inaccuracy in the measured ground truth profile; performing a ground truth profile correction for all measured ground truth profiles from among the plurality of the measured ground truth profiles for which the PDI value exceeds the threshold of distortion, via the one or more hardware processors, wherein the ground truth profile correction is iteratively performed till the PDI value is below the threshold of distortion; performing an instance selection, via the one or more hardware processors, wherein performing the instance selection comprises selecting a plurality of instances of data from among the plurality of the measured ground truth profiles, after performing the ground truth profile correction, wherein each of the plurality of instances of data is a subset of the plurality of the measured ground truth profiles; and retuning at least one of a data-driven model and a physics-based model using the plurality of selected instances and the integrated data, via the one or more hardware processors. pre-processing the input data, via the one or more hardware processors, wherein the pre-processing of the input data comprises conditioning the input data to satisfy one or more pre-defined data quality requirements; obtaining, via the one or more hardware processors, simulated data based on the pre-processed data using at least one soft sensor, wherein the at least one soft-sensor comprises a physics-based soft sensor and a data-driven soft sensor, wherein the simulated data is integrated with pre-processed data to obtain integrated data; determining a Profile Deviation Index value for each of the plurality of the measured ground truth profiles in the integrated data, via the one or more hardware processors, wherein the PDI value represents a quantified distortion in each of the plurality of the measured ground truth profiles; comparing the PDI value of each of the plurality of the measured ground truth profiles with a threshold of distortion, via the one or more hardware processors, wherein the PDI value exceeding the threshold of distortion for any of the plurality of the measured ground truth profiles indicates inaccuracy in the measured ground truth profile; performing a ground truth profile correction for all measured ground truth profiles from among the plurality of the measured ground truth profiles for which the PDI value exceeds the threshold of distortion, via the one or more hardware processors, wherein the ground truth profile correction is iteratively performed till the PDI value is below the threshold of distortion; performing an instance selection, via the one or more hardware processors, wherein performing the instance selection comprises selecting a plurality of instances of data from among the plurality of the measured ground truth profiles, after performing the ground truth profile correction, wherein each of the plurality of instances of data is a subset of the plurality of the measured ground truth profiles; and retuning at least one of a data-driven model and a physics-based model using the plurality of selected instances and the integrated data, via the one or more hardware processors.