Traditional two-factor authentication (2FA) has been widely adopted as a security mechanism, yet it remains vulnerable to phishing, brute-force, and man-in-the-middle attacks [2]. To address these weaknesses, three-factor authentication (3FA) has emerged, incorporating an additional factor such as a biometric, a graphical password, or a cryptographic technique. However, existing 3FA models suffer from usability issues, speed limitations, and lack of adaptability across diverse platforms. This study proposes an adaptive 3FA model that leverages contextual awareness, device capability, and user behaviour to dynamically select authentication factors. By integrating biometrics, graphical methods, and cryptographic techniques, the proposed model aims to balance security, efficiency, and usability. The findings are expected to contribute to the development of scalable and user-friendly authentication systems that resist modern cyberattacks.