Intelligent ecosystems depend on data that is both accurate and trustworthy. This talk follows the path from raw signals to clinical decisions, and then asks how those decisions can be kept secure in a quantum era.The first part covers AI-powered optical sensing. It looks at Raman and SERS sensors, noninvasive near-infrared glucose sensing with lightweight shallow dense neural networks suited to portable, low-power devices, and AI-enhanced nanobiosensors for the kidney injury biomarker NGAL. It also covers functional near-infrared spectroscopy (fNIRS), where deep learning turns oxy- and deoxy-hemoglobin signals into estimates of brain activity and mental state. These support early detection and continuous monitoring of cancer, diabetes, kidney disease, and brain function.The second part covers quantum computing and cryptography. Health and neural data are sensitive and long-lived, so they need protection that will outlast today's public-key schemes. The talk presents recent work on:

• Hybrid QKD–ML-KEM key establishment

• Chaos-based post-quantum cryptographic constructions

• Quantum and post-quantum blockchain and forensic ledgers for tamper-evident records

• Quantum error correction and type-safe quantum programming

Across both parts, the talk discusses reproducibility, calibration, validation, interpretability (including motion artifacts and subject variability in wearable fNIRS), data security, and deployment in community healthcare. It closes with directions toward portable, noninvasive, quantum-secure intelligent health systems that suit national-scale ecosystems such as India's.