The rapid evolution of cyber threats has led to the emergence of zero-day attacks that exploit previously unknown vulnerabilities, rendering conventional static defense mechanisms inadequate. To address this challenge, this research presents a self-learning cyber defense framework that integrates adaptive artificial intelligence with automated data science pipelines for real-time zero-day threat prediction. The proposed system continuously monitors network behavior, extracts dynamic features, and employs an adaptive learning engine capable of updating its detection models without manual intervention. A fully automated data pipeline handles data ingestion, preprocessing, feature optimization, and model retraining, ensuring continuous adaptability to evolving threat landscapes. Experimental evaluations conducted on benchmark datasets such as CICIDS2017 and UNSW-NB15 demonstrate significant improvements in detection accuracy and response latency compared to traditional intrusion detection systems. The results highlight that the proposed adaptive AI framework not only enhances predictive capability but also reduces false alarms through self-optimization and contextual learning. This study contributes a novel and scalable approach for cyber defense systems, capable of autonomously evolving in the face of unknown attack vectors, thereby strengthening organizational resilience against emerging zero-day exploits.

Keywords: Adaptive AI, Self-Learning Systems, Cyber Defense, Zero-Day Threats, Automated Data Pipelines, Anomaly Detection, MLOps