Healthcare data is emerging as the soul of the cyberattack due to information system security in the health system. AI and ML are new and promising technologies that can improve the protection of digital systems in healthcare organizations. This paper becomes a literature review of how AI and ML applications can reinforce cybersecurity in health systems. They go to how these technologies are being deployed in areas including anomaly detection, intrusion prevention, and threat intelligence and accentuate how they provide capabilities to recognize and disable risk in real-time. These concerns include data privacy, training, and high false-positive rates. This paper also examines some exemplification by giving examples of how AI and ML solutions can work in practice. Further, it suggests avenues of work, such as how the advancements in AI can be adopted in the healthcare sector and what sort of cooperation is required to create enhanced cybersecurity developments. The study points to AI and ML's impossibility of implementing sensitive health data protection and maintaining system security.