The healthcare industry is undergoing a transformation towards personalized, patient-centric models, driven by advancements in Artificial Intelligence (AI), the Internet of Things (IoT), and other digital technologies. This research explores the integration of AI and IoT in healthcare, emphasizing the shift from hospital-centered to patient-centric care. The paper presents the “PatientE” framework, a novel system designed to enhance patient care through real-time monitoring, predictive analysis, and personalized treatment plans. By incorporating machine learning algorithms like Random Forest and BiLSTM, the framework optimizes healthcare delivery, reduces treatment costs, and improves patient outcomes. Furthermore, the study discusses the ethical, regulatory, and technical challenges, such as data security, interoperability, and AI integration, while also highlighting the potential for these technologies to address issues like chronic disease management and mental health diagnostics. The findings underscore the transformative potential of AI, IoT, and machine learning in shaping the future of healthcare, offering a roadmap for implementing AI-driven personalized care systems.