The aim of this study is to conserve the primordial knowledge for the reason that the binaries of indigenous knowledge are either orally-transmitted or transmitted through imitation and demonstration. Writing it down (with their equal graphical relevance) changes some of its fundamental properties, because indigenous knowledge is orally monopolized. The paradox is that, even in contemporary automated epistemology, the indigenous knowledge shuns any form of preservation through libraries or in raw manuscripts. Thus, it means that there are no formal mechanisms or no vital area to preserve this knowledge. In the phase of drowning resources scientists, researchers, practitioners (engrossed to unearth the value of indigenous stigmata) have no path to manage the things back to their ontological trace; they always need some guidance about how the things can be resolved, and this changes the chronological documentation. However, in the 21st century, due to the ascendancy of data centric technicity, there seems to be a way to preserve the orally commuted knowledge system through Artificial Neural Networking (ANN).  Design/ Methodology/ Approach: The design of this study frames out the preservation methods and the process of preservation where the researcher can identify the way to protect the primordial knowledge in view of the fact that the Artificial Neural Network is a set of Algorithms which deals with pattern recognition, image identification, and machine translation. But, its centricity lies in its assimilation of data-processing which mirrors the information processing of the human brain. Just as the human brain follows neuronal probing for its acquiring of new stimuli and the deletion of the old ones; the ANN also modifies itself by counting the variables and their practical usability. The Encoding of data in the neurons (nodes) according to which the processing of information performed by the different algorithms with distinct functioning or implementation of techniques will regulate the working and incremental data received.  Findings: This paper explains how to preserve the Indigenous knowledge in a digital manner with the help of the Artificial Neural Network (ANN). The applicability of ANN is not wholly developed and is still in progress. Therefore, by coming away from traditional normative translation of oral words into written form - the essence of Indigenous knowledge must be preserved through some other means. The Preservation of this knowledge will help to secure the ancient property with the help of technology through which we are able to secure our future.  Originality: This paper will present its mechanisms of ‘datapreservations’ ranging from clustering to feedback algorithms; which will be crucial in the approach of indigenous knowledge preservation. The preserved knowledge, as it is dynamically opposed to the traditional mode of preservations through physical manner – can be accessed to a wide era of civilization, contributing to the creation of an equal hemisphere with rich data sets.  Research Limitation/ Implication: The main limitation of this study is that it focuses the application of the Artificial Neural Network (ANN), but this study is not limited to the theoretical analysis of this mechanism. Therefore, the future implication of this study direct towards a clear practical view of this study which helps many researchers, scientists and the practitioners to change the reality.

Keywords : Primordial Knowledge, Artificial Neural Network, Digital Sustainability, Digital Preservation, Artificial Intelligence.