International Research Journal of Education and Technology

The evolution of technology has significantly transformed democratic processes, leading to the development of online voting systems that promise enhanced accessibility, transparency, and efficiency. This paper presents the design and implementation of an efficient online voting application leveraging machine learning techniques to ensure secure, reliable, and tamper-proof elections.

The proposed system integrates advanced authentication mechanisms using machine learning-based biometric verification, such as facial recognition or fingerprint analysis, to prevent voter impersonation. A robust anomaly detection model is incorporated to identify and mitigate suspicious voting patterns, ensuring the integrity of the electoral process. Additionally, natural language processing (NLP) algorithms are utilized for real-time analysis of voter queries and feedback, enabling dynamic support and system improvement.

By employing state-of-the-art encryption methods alongside machine learning models, the application guarantees secure data transmission and storage, safeguarding voter privacy. The system is designed to handle high traffic efficiently, ensuring scalability and seamless user experience during large-scale elections.

Extensive testing demonstrates the system's resilience against cyber-attacks, accuracy in voter authentication, and its capacity to provide fair and transparent election outcomes. This innovative approach represents a significant step toward modernizing voting systems, fostering public trust in electoral processes, and supporting democratic governance in the digital age.

Keywords: online voting, machine learning, biometric authentication, anomaly detection, electoral security, transparency.