The U.S. healthcare system has been experiencing an increasingly cybersecurity crisis, with more than 276 million individuals having had their data stolen in 2024 and hacking-related breaches accounting for almost 80% of reported incidents. Traditional security measures have failed to combat advanced cyber threats that seek to steal sensitive PII, health insurance information, and medical infrastructure. This study analyzes the creation, deployment and operational outcomes of AI-driven cyber-risk prediction models for U.S. healthcare institutions. A systematic literature review (SLR) design was used to analyze peer-reviewed academic journals and cybersecurity reports between 2012 to 2025 in databases such as IEEE Xplore, PubMed and ACM Digital Library. The study compares machine learning approaches for supervising the learning process, deep learning models and natural language processing tasks in healthcare cybersecurity. The findings indicated that AI models show superior performance in threat and risk prediction, particularly with gradient boosting algorithms, which yield the best accuracy for vulnerability identification. However, there continue to be barriers to implementation such as resource limitations, existing infrastructure needs, workforce skill gaps and regulatory uncertainty. Budget allocation is found to be the most important determinant of AI adoption success. AI technology has the potential to transform health care cybersecurity, but true investment value will only be seen when the necessary infrastructures are in place through strategic investments, training of staff and people with IT expertise, policy harmonization (both regulatory and policy), inclusive collaborative frameworks that support collective defense and are HIPAA compliant, while taking into account patient privacy issues.