Machine learning has emerged as a transformative force in cybersecurity, enabling predictive defence mechanisms that move beyond traditional reactive strategies. This review explores the evolution, methodologies, and applications of machine learning models in predictive cybersecurity defence. By leveraging large-scale data, these models can detect anomalies, anticipate threats, and automate responses in real time. Techniques such as supervised learning, unsupervised learning, and deep learning have been widely adopted to identify patterns in network traffic, user behaviour, and system logs. Predictive capabilities allow organizations to mitigate risks before attacks occur, reducing financial and operational damage. However, challenges such as adversarial attacks, data imbalance, model interpretability, and scalability persist. This article also highlights emerging trends, including federated learning, explainable AI, and hybrid defence systems that integrate human expertise with machine intelligence. Through a comprehensive analysis, the review emphasizes the need for robust, adaptive, and ethical frameworks to ensure reliable deployment of machine learning in cybersecurity. The findings suggest that while machine learning significantly enhances predictive capabilities, its effectiveness depends on data quality, continuous model updates, and integration with existing security infrastructures.