Large groups can make decisions via techniques like voting, referendums, and elections. During the realization and evaluation of these events, time efficiency, counting integrity, and voting reliability are crucial factors. Users can create their own polls, votes, and surveys using the interfaces created in this study. On these designed election processes, they can cast an electronic ballot. The CNN machine learning system evaluates the votes, or the counting process, with great accuracy, preventing manipulation and bias. Blockchain and zero-knowledge proof-based infrastructures support evaluation processes concurrently, enhancing voting privacy, data security, and transparency procedures. The results of the proposed CNN algorithm and the data privacy metrics demonstrate satisfactory performance.