Machine learning for cybersecurity is a rapidly evolving research area focused on developing intelligent systems to detect, prevent, and respond to cyber threats across networks, applications, and IoT environments. Research papers in this domain explore supervised, unsupervised, and reinforcement learning techniques for applications such as intrusion detection, malware analysis, phishing detection, anomaly detection, DDoS attack mitigation, and fraud detection. Traditional machine learning approaches include support vector machines (SVM), decision trees, random forests, k-nearest neighbors (KNN), and ensemble methods, while advanced studies employ deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), autoencoders, and generative adversarial networks (GANs). Key contributions include feature engineering, behavior modeling, real-time threat detection, and integration with edge/fog computing for low-latency responses. Recent research also addresses challenges such as class imbalance, adversarial attacks, scalability, and privacy-preserving learning using federated approaches. By leveraging machine learning, cybersecurity research aims to provide proactive, adaptive, and intelligent defense mechanisms that enhance network resilience and protect sensitive data from evolving threats.