Abstract The main issue for any organisational elections is the validity and accessibility of the electoral roll. The electoral roll requires data validation when data is added or updated, and requires effective data selection when data retrieval operations are required. It is an evolving process that needs continuous moderation from both the bureaucrats and the public. Because of their inherent properties, such as non-mutability, multi-level authentication, cryptography, and strict data update laws, Blockchain driven systems have proven to be efficient in terms of security. But due to large data chains, blockchain systems have limited retrieval performance, which slows down the read-cycles, rendering the system ineffective for big data applications. A side-chain-based solution for the efficient storage and recovery of electoral roll data is described in this paper. In comparison to single-chain systems, data updating and insertion into side chains is less reliable. This is due to the fact that as opposed to their single chain counterparts, side chains are very limited in scale, so targeting these side chains is simpler. In order to preserve the security of the side-chain scheme, this paper describes a deep learning cryptosystem (DLC). In order to create a special non-identifiable side chain structure, this DLC utilises a combination of adaptive cryptography and transfer learning. In order to ensure high fidelity and reduce the risk of attacks, this side-chain system uses a hybrid blockchain implementation. The proposed method, while retaining the same degree of accuracy and protection, is found to be 47 percent faster than the current single-chain electoral roll management solutions.