Computational thinking (CT) has emerged as a transformative approach in healthcare education by fostering analytical reasoning, structured problem-solving, and data-driven decision-making. This study proposes and evaluates CT-enhanced frameworks that integrate feature extraction, clustering, probabilistic scoring, predictive modeling, and iterative validation to advance both teaching and clinical training outcomes. By leveraging AI-supported tools, simulation modeling, and real-time feedback mechanisms, the approach emphasizes continuous improvement and adaptability. Results show substantial improvements in cognitive skills, such as learning outcomes, problem-solving, and critical thinking, along with behavioral factors, including engagement, knowledge retention, and practical application. Predictive modeling demonstrated 95% accuracy, with precision and recall exceeding 88%, while maintaining computational efficiency. The methodology not only improved educational performance but also prepared healthcare students for real-world challenges by equipping them with the skills to interpret complex datasets, apply AI-driven reasoning, and adapt to evolving clinical contexts.