Among the most prevalent cyber threats, phishing attacks are still state-of-the-art and, thus, difficult to detect, such as zero-day phishing attacks. Most of the conventional phishing detection mechanisms rely on signature-based or rule-based systems that cannot identify such new emerging threats and neutralize them in real time. The paper proposes applying automated artificial intelligence (AI)-driven approaches for detecting and responding to zero-day phishing attacks. Therefore, this research suggests developing a robust phishing attempts detection system, identifying phishing through diverse digital platforms, such as emails, websites, and social media, using advanced machine learning techniques that include but are not limited to Natural Language Processing (NLP), image recognition, and anomaly detection. The proposed system will integrate supervised and unsupervised learning models within a framework that will deliver real-time countermeasures, including automated user alerts and blocking mechanisms. Experimental results are presented that evaluate the detection accuracy, false favorable rates, and speed of response by comparing performance with existing methods. These results show in detail how well the AI-driven approach reduces the success rate of zero-day phishing attacks and proves to be more adaptive and scalable than traditional methods. This research forms the basis for understanding the full potential of AI-based systems for cybersecurity defenses and resiliency while setting the ground for future developments in automated phishing countermeasurement research that may impact sensitive digital infrastructures.