The integration of Artificial Intelligence (AI) with the Internet of Things (IoT), commonly referred to as the Artificial Intelligence of Things (AIoT), enables intelligent, continuous patient monitoring and predictive healthcare. Conventional healthcare monitoring systems primarily collect physiological parameters and generate alerts after abnormal conditions occur. The proposed AIoT-based smart healthcare system integrates wearable IoT sensors, edge computing, cloud infrastructure, and machine learning to support continuous monitoring and low-latency healthcare analytics. Connected sensing devices continuously collect physiological parameters such as heart rate, blood oxygen saturation, body temperature, respiratory rate, and blood pressure. An edge-processing layer performs preprocessing, noise removal, feature extraction, and preliminary anomaly detection, while cloud-based AI models perform predictive analytics on historical and streaming patient data. Machine learning models such as Random Forest, Support Vector Machine, XGBoost, and Long Short-Term Memory networks can be evaluated for risk prediction. The proposed architecture also incorporates an intelligent alert mechanism and a healthcare dashboard for patients, caregivers, and clinicians. The framework aims to support early risk identification and responsive healthcare decision-support. Security, privacy, interoperability, explainability, and scalability are considered important design requirements for practical AIoT healthcare deployment.