The rapid advancement of the Internet of Things (IoT) has transformed healthcare systems by enabling continuous monitoring, intelligent automation, and real-time patient assistance. However, existing IoT healthcare frameworks face significant challenges related to scalability, data security, explainability of AI decisions, energy efficiency, and interoperability among heterogeneous medical devices. This paper proposes a novel Explainable Artificial Intelligence (XAI)-enabled IoT framework for smart healthcare environments that integrates edge computing, deep learning, federated learning, and lightweight security mechanisms for real-time predictive healthcare monitoring. The proposed architecture utilizes wearable sensors and intelligent medical devices to collect physiological data such as heart rate, oxygen saturation, body temperature, respiratory rate, and ECG signals. Edge-based AI models perform local data analysis to reduce latency and bandwidth consumption, while explainable AI techniques improve transparency and trust in automated clinical decision-making. The framework further incorporates blockchain-assisted secure communication and adaptive anomaly detection for protecting sensitive healthcare information against cyber threats. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, enhanced interpretability, and lower computational overhead compared to traditional cloud-centric IoT healthcare systems. The proposed model offers a scalable and energy-efficient solution suitable for remote patient monitoring, elderly care, smart hospitals, and telemedicine applications. This research contributes toward the development of intelligent, trustworthy, and sustainable next-generation healthcare ecosystems