Blockchain-enabled telehealth systems with cryptographic protection for physiological sensor data, energy constraints of implant-class wearables, and GDPR compliance have revolutionized patient monitoring and data management. However, AES encryption through Ethereum’s LSTM-based ECG prediction exhibits critical problems, such as prohibitive blockchain costs, single-patient generalization failure, invalidated wearable energy, absence of key recovery, lack of EHR interoperability, unmeasured end-to-end latency, no collusion resistance, and the conflict of the GDPR Article 17. This paper proposes a new Hyperledger Fabric RAFT consortium blockchain, FedAvg of federated learning, quantized TCN INT8 key generation, 3-of-5 Shamir Secret Sharing to protect sensitive data, HL7 FHIR R4 gateway, hash-based ZK commitment for authentication, six-stage pipeline benchmarking for throughput, and chameleon hash to erasure GDPR. Hyperledger Fabric RAFT achieves 63.3± 4.1 ms block commit latency at 0.00 per-transaction fee (98.4% improvement). FedAvg federated learning on the MIT-BIH ECG yields 71.8% reduction in MAE on three nodes without raw data transmission. A quantized TCN INT8 model reduces the scaled ARM Cortex-M4 inference energy from 22.50μ J (BeDaSH estimate) to 0.06μ J (MLPerf Tiny scaled estimate), a 99.7% reduction, with all 1000 generated keys confirmed unique on the measurement platform. A 3-of-5 Shamir scheme over F2127-1) enables key recovery in 37.7μs. HL7 FHIR R4 validation yields 10/10 HTTP 201 responses. Cryptographic pipeline benchmarking shows a P50 latency of 2.7 ms and 317.5 TPS at ten concurrent sessions; including the Fabric commit, full-path P50 is 66.0 ms. ZK-proof authentication blocks 100/100 collusion attacks. Chameleon hash erasure implements the recommended architectural pattern of EDPB Guidelines 02/2025 for GDPR Article 17 compliance in 100 patient records.