The large-scale application of IoT technology in the medical industry also brings the advantage of continuous monitoring of patients and timely detection of heart diseases; however, it also results in challenges like ensuring data privacy, resources, and management of medical records. The paper presents a secure hybrid machine learning framework incorporated with IoT for diagnosing heart diseases at their infant stage, mostly meant for healthcare settings with low resources. The proposed protocol integrates lightweight encryption to secure medical data during early-stage diagnosis and data transmission in IoT-based healthcare systems combined with machine learning models, while nature-inspired optimization improves feature selection, classifier tuning, accuracy, and convergence. A variety of cardiac datasets are extensively used for the system's proposal, and it is evaluated under inconsistent and noisy IoT conditions to determine its generalization capacity, robustness, and interpretability, which are assessed through the deployment of explainable artificial intelligence techniques. The framework is also analyzed regarding scalability, computational efficiency, and security - performance trade-offs to verify its suitability for real-time deployment in large-scale IoT healthcare systems. The findings of the study indicate that the suggested method has a well-balanced integration of security, accuracy, interpretability, and efficiency, thereby giving a practical and reliable solution for intelligent IoT-based healthcare diagnostics.
