A processor implemented method, comprising: receiving, by one or more hardware processors, multivariate time series data pertaining to an industrial unit; subsetting, by the one or more hardware processors, a plurality of key variables from the f multivariate time series data, the method being characterised in that it further comprises the steps of: segmenting, by the one or more hardware processors, the multivariate time series data pertaining to the plurality of key variables based on a predefined window size; computing, by the one or more hardware processors, masked time series data by applying a mask on the segmented time series data, wherein the mask corresponds to a presence or an absence of data in the segmented multivariate time series data; constructing, by the one or more hardware processors, feed data by concatenating a plurality of metadata associated with the industrial unit with the masked time series data, wherein the metadata comprises qualitative and quantitative data pertaining to the industrial unit; computing, by the one or more hardware processors, reconstructed data corresponding to the masked time series data by utilizing a pre-trained machine learning architecture, wherein the pre-trained machine learning architecture is selected based on a model recommendation, wherein the reconstructed data is computed based on the feed data computing, by the one or more hardware processors, a reconstruction error matrix based on the masked time series data and the reconstructed data, wherein a reconstruction error associated with missing data is removed by utilizing the mask; computing, by the one or more hardware processors, a mean reconstruction error associated with each of a plurality of sensors of the industrial unit, wherein the mean reconstruction error is computed based on the computed reconstruction error matrix; and detecting, by the one or more hardware processors, an anomaly associated with each of the plurality of key variables based on the computed mean reconstruction error associated with each of the plurality of sensors, wherein the computed mean reconstruction error associated with each of the plurality of sensors is compared with a corresponding predetermined mean reconstruction error threshold. receiving, by one or more hardware processors, multivariate time series data pertaining to an industrial unit; subsetting, by the one or more hardware processors, a plurality of key variables from the f multivariate time series data, segmenting, by the one or more hardware processors, the multivariate time series data pertaining to the plurality of key variables based on a predefined window size; computing, by the one or more hardware processors, masked time series data by applying a mask on the segmented time series data, wherein the mask corresponds to a presence or an absence of data in the segmented multivariate time series data; constructing, by the one or more hardware processors, feed data by concatenating a plurality of metadata associated with the industrial unit with the masked time series data, wherein the metadata comprises qualitative and quantitative data pertaining to the industrial unit; computing, by the one or more hardware processors, reconstructed data corresponding to the masked time series data by utilizing a pre-trained machine learning architecture, wherein the pre-trained machine learning architecture is selected based on a model recommendation, wherein the reconstructed data is computed based on the feed data computing, by the one or more hardware processors, a reconstruction error matrix based on the masked time series data and the reconstructed data, wherein a reconstruction error associated with missing data is removed by utilizing the mask; computing, by the one or more hardware processors, a mean reconstruction error associated with each of a plurality of sensors of the industrial unit, wherein the mean reconstruction error is computed based on the computed reconstruction error matrix; and detecting, by the one or more hardware processors, an anomaly associated with each of the plurality of key variables based on the computed mean reconstruction error associated with each of the plurality of sensors, wherein the computed mean reconstruction error associated with each of the plurality of sensors is compared with a corresponding predetermined mean reconstruction error threshold.