User authentication is critical in modern cybersecurity frameworks. Traditional methods like passwords and PINs are increasingly vulnerable to phishing, reuse, and brute-force attacks. This paper introduces an advanced hybrid authentication framework that combines AI-driven facial recognition, blockchain-enabled device authentication, adaptive liveness detection, and behavioral biometrics. The proposed framework addresses not only traditional threats but also anticipates emerging attack vectors in increasingly interconnected environments. Extensive evaluations using large-scale datasets demonstrate a 98.5% authentication accuracy with sub-second latency, leveraging edge computing. The integration of edge computing significantly reduces reliance on centralized servers, improving scalability and responsiveness. The system is further analyzed for privacy preservation, statistical robustness, and operational efficiency. Additionally, cross-platform interoperability and scalability across diverse devices were considered to ensure broad applicability. Future directions include enhancing resource optimization, and user experience, and incorporating post-quantum cryptographic techniques. As the cybersecurity landscape evolves, such multifaceted approaches are essential to achieving resilient, user-friendly authentication mechanisms.
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