A processor implemented method for identification of compounds for hydrogen storage, the method comprising: providing, via an input module, a first set of one or more elements ; providing, via the input module, a second set of one or more elements ; generating, via one or more hardware processors, a plurality of compounds using the first set of one or more elements and the second set of one or more elements based on predefined input conditions, wherein the plurality of compounds have hydrogen storage capabilities ; generating, via the one or more hardware processors, molecular descriptors for the plurality of compounds using compositional information of each of the plurality of compounds ; generating, via the one or more hardware processors, a machine learning model using the molecular descriptors ; predicting, via the one or more hardware processors, gravimetric hydrogen storage capacity of the generated plurality of compounds using the machine learning model ; selecting, via the one or more hardware processors, a set of compounds out of the plurality of compounds based on a predefined criteria of gravimetric hydrogen storage capacity ; predicting, via the one or more hardware processors, formation energy of each of the set of compounds using a computational database of formation energies ; calculating, via the one or more hardware processors, minimum formation energy of all possible decomposition mixtures of each compound ; and identifying, via the one or more hardware processors, the compound as stable if the formation energy of the compound is less than the minimum of the formation energies of the possible decomposition mixtures of the respective compound, wherein each of the stable compounds is identified as the compound for hydrogen storage . providing, via an input module, a first set of one or more elements ; providing, via the input module, a second set of one or more elements ; generating, via one or more hardware processors, a plurality of compounds using the first set of one or more elements and the second set of one or more elements based on predefined input conditions, wherein the plurality of compounds have hydrogen storage capabilities ; generating, via the one or more hardware processors, molecular descriptors for the plurality of compounds using compositional information of each of the plurality of compounds ; generating, via the one or more hardware processors, a machine learning model using the molecular descriptors ; predicting, via the one or more hardware processors, gravimetric hydrogen storage capacity of the generated plurality of compounds using the machine learning model ; selecting, via the one or more hardware processors, a set of compounds out of the plurality of compounds based on a predefined criteria of gravimetric hydrogen storage capacity ; predicting, via the one or more hardware processors, formation energy of each of the set of compounds using a computational database of formation energies ; calculating, via the one or more hardware processors, minimum formation energy of all possible decomposition mixtures of each compound ; and identifying, via the one or more hardware processors, the compound as stable if the formation energy of the compound is less than the minimum of the formation energies of the possible decomposition mixtures of the respective compound, wherein each of the stable compounds is identified as the compound for hydrogen storage . <EOS>