Cybercrime is growing rapidly and takes advantage of weaknesses in today’s computing systems. Ethical hackers play an important role in identifying these weaknesses and proposing effective methods to reduce security risks. As cyber threats continue to evolve, the cybersecurity community faces an urgent need for advanced and reliable protection techniques.In recent years, machine learning has become increasingly important in cybersecurity because of its ability to analyze large amounts of data and identify complex attack patterns. Machine learning approaches are commonly applied to key security tasks such as intrusion detection, malware detection and classification, spam filtering, and phishing detection.While machine learning alone cannot fully automate cybersecurity operations, it significantly improves the efficiency and accuracy of threat detection compared to traditional rule-based methods, thereby reducing the workload of security professionals.The constantly changing nature of cyber threats presents ongoing challenges for researchers, requiring a strong combination of expertise in both cybersecurity and data science. This paper reviews recent machine learning-based cybersecurity solutions and examines the effectiveness of various algorithms in addressing common cyber threats.