The increasing Internet of Things (IoT) integration of consumer healthcare devices raises serious cybersecurity and data privacy issues. The current research uses Artificial Intelligence of Things (AIoT) technology to present an anomaly detection system that addresses these issues. Using machine learning techniques, the framework offers a proactive security solution for consumer healthcare devices by identifying and reducing cyber threats in real time. The SCADA and BoT-IoT datasets, which concentrate on cybersecurity vulnerabilities in healthcare-related networks, are used for experimental validation even though the framework is intended for protecting consumer healthcare IoT devices. Using the SCADA and BoT-IoT datasets, the framework achieves an anomaly detection accuracy of 99%, outperforming existing techniques in terms of false alarm rate (0.1%), scalability (95%), and real-time threat detection (98%).