Modern enterprise networks generate a large volume of security events, making it difficult for security analysts to identify critical threats in real time. Traditional rule-based detection mechanisms often fail to detect advanced and evolving cyber attacks. Artificial Intelligence (AI) and Machine Learning (ML) techniques have shown promising capabilities in analyzing large-scale security data and predicting potential cyber threats. This research proposes an AI-based cyber threat prediction framework designed to enhance threat detection and decision-making in enterprise environments. The framework focuses on log analysis, anomaly detection, and threat prediction using machine learning techniques. The study highlights the potential of predictive analytics in improving proactive cybersecurity strategies and reducing response time in security operations centers (SOCs). The proposed framework is conceptual and aims to provide a cost-effective and scalable approach for organizations adopting intelligent cybersecurity solutions.