Abstract: This project focuses on how Radial Basis Function (RBF) neural networks could be used in intelligent voting systems for facial identification. One application where face recognition is starting to show its value in terms of improved security and effectiveness is election procedures. Two essential components of these processes are voter verification and fraud prevention. Utilizing RBF face recognition as its foundation, an intelligent voting system tackles many noteworthy obstacles. This protects the integrity of voting proceedings by reducing the likelihood of voter impersonation and election fraud. The project uses case studies and empirical assessments to show how effective the proposed strategy is. According to research, RBF neural networks excel in face recognition tasks, achieving high accuracy rates across a wide range of datasets. Prototyping and simulations indicate the smart voting system's scalability and applicability in a variety of political settings.

Keywords: Radial Basis Function (RBF) neural networks, Intelligent voting systems, Facial identification, Voter verification and fraud prevention.

| DOI: 10.17148/IARJSET.2025.125247