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Zero-day attacks are among the most critical cybersecurity threats as they exploit unknown vulnerabilities in software systems before patches are available. Traditional security mechanisms fail to detect such attacks due to their reliance on known signatures. This paper proposes an intelligent system that integrates Artificial Intelligence (AI) with Multi-Factor Authentication (MFA) to enhance both detection and prevention of zero-day attacks. The system uses machine learning algorithms, particularly Isolation Forest, to analyze user behavior and detect anomalies in real time. Additionally, MFA ensures secure access by requiring multiple verification factors such as passwords and one- time passwords. The proposed approach improves detection accuracy, reduces unauthorized access, and provides a robust solution for modern cybersecurity challenges