The combination of Internet of Things (IoT) technologies and machine learning (ML) has opened new possibilities for predictive and personalized healthcare. This research explores how users perceive IoT-enabled healthcare systems, focusing on accuracy, trust, and privacy, which are critical factors for adoption. A quantitative survey involving 207 participants was carried out to analyze device usage trends, the reliability of health metrics, and how people respond behaviorally to predictive feedback. The findings reveal that while participants showed considerable confidence in metrics like heart rate and physical activity, they were more doubtful about advanced measurements such as blood pressure and electrocardiogram (ECG) monitoring. Privacy and data security were identified as the most important factors influencing trust, followed by the importance of medical validation and clarity in predictive results. Furthermore, insights from IoT devices were found to positively affect preventive health behaviors, including a rise in physical activity, dietary changes, and more frequent medical consultations. To address these insights, this paper suggests a framework that prioritizes trust, protects privacy, and adapts behavior, integrating explainable artificial intelligence (XAI), federated learning, and adaptive feedback mechanisms. By aligning technical precision with user-focused needs, the framework lays the groundwork for creating scalable, transparent, and secure predictive healthcare solutions.