- When it comes to providing appropriate protection against sophisticated assaults, standard cybersecurity methods often fall short in the constantly shifting environment of cyber threats. This article investigates the use of Machine Learning (ML) strategies in the field of cybersecurity with the goal of improving capabilities in the areas of threat detection, prevention, and response capacity. We address major machine learning methodologies, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning, as well as their applications in areas such as the detection of intrusions, the analysis of malware, the detection of phishing, and the prevention of fraud. The obstacles of adopting machine learning in cybersecurity are also discussed in the research. These issues include challenges related to data quality, model performance, and interpretability. Through the use of machine learning, cybersecurity systems are able to reach improved levels of accuracy, flexibility, and resilience, therefore providing a significant defence against new threats.