This research study introduces a comprehensive methodology for early heart disease prediction through the integration of Internet of Things (IoT) health records and advanced ensemble deep learning techniques. The proposed system combines a novel hybrid architecture that synthesizes continuous monitoring data from wearable devices with traditional clinical parameters, incorporating real-time physiological measurements, historical patient data, and environmental factors. Our ensemble approach leverages a three-tier architecture: a Long Short-Term Memory (LSTM) network for temporal pattern recognition, a Convolutional Neural Network (CNN) for ECG feature extraction, and a Gradient Boosting Machine (GBM) for structured clinical data analysis. The system achieves 94.2% accuracy in early detection of cardiac events, with a mean prediction lead time of 6.8 h, significantly improving upon existing methodologies that typically provide 2–3 h warning windows. Validation across a diverse cohort of 10,000 patients from multiple healthcare facilities demonstrated robust performance, maintaining accuracy above 92% across different demographic groups, comorbidity profiles, and environmental conditions. The system’s real-time processing capability enables continuous risk assessment with a latency of less than 100 ms, making it suitable for clinical deployment. Implementation in five major hospitals showed a 45% reduction in false alarms compared to traditional monitoring systems, while maintaining high sensitivity (92.8%) and specificity (95.6%) in identifying potential cardiac events. These results, combined with the system’s scalable architecture and HIPAA-compliant data handling, present a significant advancement in preventive cardiac care and demonstrate its viability for large-scale clinical applications.