Electronic Health Records (EHRs) are critical components of modern healthcare systems, but ensuring data security, privacy, and real-time clinical decision-making remains a significant challenge. This study proposes an advanced AI-based EHR architecture that prioritizes secure patient data while delivering intelligent clinical analytics. Homomorphic Encryption (CKKS), Attribute-Based Encryption (ABE) for fine-grained access control, and Hyperledger Fabric for blockchain-based audit records are some of the security features that are built into the system. Differential Privacy (DP-SGD) is used to make privacy even stronger when AI models are being trained. It uses an AI engine that can analyse medical transcripts using Convolutional Neural Networks (CNNs) for image-based detection (X-ray, MRI) and advanced signal processing models for reading ECG and EEG data. This AI-powered system works with different types of data and uses models like ClinicalBERT, GatorTron, GRU-D, and LightGBM to handle medical pictures, bodily signs, time-series vitals, and clinical notes. Experimental results demonstrate that the proposed architecture achieves low encryption latency (920 ms), zero access control breaches, and excellent predictive accuracy (AUC-ROC up to 0.96). This comprehensive approach demonstrates that integrating AI with robust security protocols, and multimodal medical analysis can deliver a scalable, privacy-preserving EHR solution suitable for real-world healthcare environments. The framework can further be extended with use of more safe APIs to collect real-time data from Internet of Medical Things (IoMT) devices and Digital Twin-based monitoring systems. That will help healthcare practitioners to keep an eye on patients all the time and treat accordingly, with use of our AI-Engines and secure Data of patients using blockchain will become a EHR where health data that is 100% tracked and analysed for better healthcare services, across multiple institutions.