The use of Wireless Body Area Networks (WBAN) in healthcare is transforming how patients and doctors communicate and how patient data is collected. WBAN allows for the real-time transmission of patient information to healthcare providers, reducing the need for constant in-person monitoring and hospital stays for data collection. However, WBAN technology is vulnerable to significant cybersecurity risks, such as eavesdropping and man-in-the-middle attacks, which can compromise the integrity and confidentiality of user data. Common threats include Denial of Service (DoS), Spoofing, and Replay attacks. To mitigate these risks, advanced security protocols, latency assessments, and intrusion detection systems are necessary. Machine learning and artificial intelligence (AI) in Cyber-Physical Systems (CPS) for healthcare offer promising solutions for continuous monitoring and threat prediction. AI systems can enhance security by detecting patterns indicative of cyber threats and deploying adaptive defenses. This study emphasizes the importance of robust security architectures and innovative AI applications to protect patient data in WBAN and CPS, ensuring secure and reliable healthcare services.