The rapid expansion of digital infrastructure has significantly increased the complexity and frequency of cyber threats, necessitating advanced approaches for accurate and timely attack prediction. Traditional machine learning techniques have been extensively employed to analyze network traffic and detect anomalies based on historical data, offering reliable performance in identifying known attack patterns; however, their dependence on predefined features and labelled datasets often limits their ability to generalize emerging and sophisticated threats. In contrast, recent advancements in generative artificial intelligence introduce a more adaptive paradigm by enabling models to learn underlying data distributions and generate realistic representations of potential attack scenarios, thereby enhancing the capability to anticipate previously unseen vulnerabilities. This paper examines the evolution from conventional machine learning methods to generative AI-driven frameworks for cyber-attack prediction, emphasizing their comparative strengths and the potential for integrated approaches to improve the robustness, adaptability, and overall effectiveness of modern cybersecurity systems in dynamic environments.