Machine learning-assisted security and privacy provisioning for edge computing is an emerging research area that focuses on leveraging intelligent algorithms to detect, prevent, and mitigate cyber threats while preserving sensitive data in distributed, resource-constrained edge environments. Research papers in this domain explore the use of machine learning, deep learning, reinforcement learning, and hybrid AI techniques to enhance intrusion detection, anomaly detection, malware detection, access control, and privacy-preserving data analytics directly at edge nodes. Studies highlight the challenges of operating in heterogeneous and dynamic edge networks, where limited computational and energy resources necessitate lightweight and adaptive models. Recent works integrate federated learning, blockchain, and secure multi-party computation with machine learning frameworks to provide collaborative, privacy-aware, and resilient security solutions. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and mobile edge computing, where real-time threat mitigation and data confidentiality are critical. Overall, machine learning-assisted security and privacy provisioning in edge computing enables intelligent, proactive, and scalable protection mechanisms that balance high detection accuracy with low latency and efficient resource utilization.