ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture.