The increasing prevalence of radiation exposure, from medical imaging to environmental and occupational hazards, necessitates advanced methods for predicting associated health outcomes. Traditional approaches to assessing radiation-induced health risks, such as dosimetry and biomarkers, often fall short in providing timely and accurate predictions. Artificial Intelligence (AI), with its capabilities in machine learning and deep learning, offers a promising solution to this challenge. This review explores the potential of AI in predicting health outcomes from radiation exposure, highlighting the integration of diverse data sources, including medical records, imaging data, genetic information, and environmental exposure data. AI algorithms, particularly supervised learning for classification and regression, and deep learning techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are increasingly being utilized to analyze complex datasets and identify patterns indicative of radiation-induced health effects. The development and training of AI models involve meticulous data preprocessing and feature selection to ensure accuracy and reliability. Case studies demonstrate AI's potential in predicting cancer risk from medical imaging and estimating exposure levels from environmental data, showcasing significant improvements in prediction accuracy compared to traditional methods. However, the application of AI in this domain is not without challenges. Data quality and availability, ethical and legal considerations, and technical integration issues pose significant hurdles. Ensuring the privacy and security of patient data, achieving regulatory compliance, and addressing the scalability and computational demands of AI models are critical factors that need to be addressed. Future advancements in AI technology, coupled with collaborative efforts between healthcare providers, researchers, and tech companies, hold the potential to revolutionize personalized medicine. By tailoring risk assessment and prevention strategies to individual patients, AI can significantly enhance the precision of healthcare interventions and improve patient outcomes. This review underscores the importance of continued research and innovation in leveraging AI to predict health outcomes from radiation exposure, paving the way for advancements in medical research and clinical practice.