The rapid growth of digital technologies and internet-based services has significantly increased the risk of cyber threats across organizations, educational institutions, financial systems, and government sectors. Traditional cybersecurity techniques often struggle to detect sophisticated and evolving attacks in real time. In this context, Machine Learning (ML) has emerged as a powerful approach for improving cybersecurity threat detection and prevention mechanisms. This research article explores the application of machine learning algorithms in identifying malicious activities, detecting anomalies, and preventing cyberattacks before they cause severe damage. Various supervised and unsupervised learning models such as Decision Trees, Support Vector Machines, Random Forest, and Neural Networks are widely used to analyze large volumes of security data and recognize suspicious patterns with high accuracy. The study also highlights the role of ML in intrusion detection systems, malware analysis, phishing detection, and network security monitoring. Furthermore, the paper discusses the advantages, challenges, and future possibilities of integrating artificial intelligence with cybersecurity frameworks. The findings indicate that machine learning-based cybersecurity systems can provide faster response, improved accuracy, and adaptive protection against emerging cyber threats in the modern digital environment.

Keywords : Machine Learning, Cybersecurity, Threat Detection, Artificial Intelligence, Intrusion Prevention