In the evolving cybersecurity landscape, traditional reactive methods are increasingly inadequate. This article explores the transformative potential of machine learning (ML) in proactive threat analysis, aiming to pre-emptively identify and neutralize threats before they emerge. By employing ML algorithms, cybersecurity systems can analyse vast datasets in real time, recognize patterns, and detect anomalies indicating potential threats. The article reviews current cybersecurity challenges, examines how ML techniques—such as decision trees, neural networks, and clustering—are utilized in threat analysis, and assesses various ML-driven cybersecurity solutions through literature, case studies, and analysis. It highlights ML's benefits, including enhanced detection accuracy, quicker responses, and future threat prediction capabilities. However, challenges such as data quality, adversarial attacks, and high computational demands are also discussed. The article concludes by addressing these limitations and suggesting that while ML offers a promising approach, its success depends on overcoming these hurdles. Emerging trends and future directions emphasize the need for continued research and development in ML for cybersecurity.