A processor implemented method for designing artificially structured materials, the method comprising: receiving, via one or more hardware processors, a set of dimension parameters from a user corresponding to a structure of the artificially structured materials wherein the set of dimension parameters refers to geometries of the artificially structured materials; creating a dataset of the geometries corresponding to the structure of the artificially structured materials using the set of requirements; training, via the one or more hardware processors, a forward model using the set of dimensional parameters as input and predicting their corresponding electromagnetic response as output using a forward model made using a deep neural network and long short term memory (LSTM) based architecture; saving, via the one or more hardware processors, weights and biases for the forward model; fixing, via the one or more hardware processors, the weights and the biases of a decoder part in an encoder-decoder tandem network from the saved weights and the biases of the forward model to control a combined loss; training, via the one or more hardware processors, the encoder-decoder tandem network for an inverse problem using a single input multiple output model with the predicted electromagnetic response as input and an output electromagnetic response at a time distributed layer of the encoder-decoder tandem network in the end and the dimension of artificially structured materials at a mid-dense layer of the encoder-decoder tandem network as output; saving, via the one or more hardware processors, the weights and the biases of the encoder part from the trained encoder-decoder tandem network; fixing, via the one or more hardware processors, the weights and the biases of the encoder from the saved weights and the biases of the trained encoder-decoder tandem network; providing, via the one or more hardware processors, the desired electromagnetic response as input to the trained encoder-decoder tandem network to obtain a final set of dimensions from the mid-dense layer of the encoder-decoder tandem network; and designing, via the one or more hardware processors, the artificially structured material using the final set of dimensions. receiving, via one or more hardware processors, a set of dimension parameters from a user corresponding to a structure of the artificially structured materials wherein the set of dimension parameters refers to geometries of the artificially structured materials; creating a dataset of the geometries corresponding to the structure of the artificially structured materials using the set of requirements; training, via the one or more hardware processors, a forward model using the set of dimensional parameters as input and predicting their corresponding electromagnetic response as output using a forward model made using a deep neural network and long short term memory (LSTM) based architecture; saving, via the one or more hardware processors, weights and biases for the forward model; fixing, via the one or more hardware processors, the weights and the biases of a decoder part in an encoder-decoder tandem network from the saved weights and the biases of the forward model to control a combined loss; training, via the one or more hardware processors, the encoder-decoder tandem network for an inverse problem using a single input multiple output model with the predicted electromagnetic response as input and an output electromagnetic response at a time distributed layer of the encoder-decoder tandem network in the end and the dimension of artificially structured materials at a mid-dense layer of the encoder-decoder tandem network as output; saving, via the one or more hardware processors, the weights and the biases of the encoder part from the trained encoder-decoder tandem network; fixing, via the one or more hardware processors, the weights and the biases of the encoder from the saved weights and the biases of the trained encoder-decoder tandem network; providing, via the one or more hardware processors, the desired electromagnetic response as input to the trained encoder-decoder tandem network to obtain a final set of dimensions from the mid-dense layer of the encoder-decoder tandem network; and designing, via the one or more hardware processors, the artificially structured material using the final set of dimensions.