Ultrasound coupling technology is pivotal to ensuring high-quality diagnostic imaging, yet conventional water-based gels face persistent challenges, including acoustic impedance mismatch, air-bubble formation, dehydration, messiness, and cross-contamination risks. This review presents a comprehensive analysis of the evolution, materials science, and clinical performance of ultrasound gel pads, an advanced alternative engineered for superior acoustic transmission, hygiene, and patient comfort. Historical progression from early coupling agents to modern polymeric and hydrogel-based pads is traced, highlighting breakthroughs such as bilayer hydrogels, nanocomposite reinforcements, metamaterial-inspired designs, and patient-specific 3D-printed pads. Comparative evaluations demonstrate that gel pads, particularly those integrating nanotechnology, rival but often outperform traditional gels in transmission efficiency, near-field resolution, and adaptability to complex anatomical surfaces, while offering reusability and reduced environmental impact. For instance, solid gel pads achieved 92.3% stone disintegration, compared with 45.5% for semi-liquid gel, in ESWL phantom studies (p < 0.001). Materials, including polyacrylamide, silicone, and advanced hydrogels, are analyzed for mechanical properties, biocompatibility, and sustainability, with emphasis on biodegradable and locally sourced alternatives. Manufacturing innovations ranging from continuous casting to additive manufacturing enable customization, functional integration, and scalable production, although cost, supply chain stability, and regulatory compliance remain critical barriers. By uniting advances in materials engineering, nanotechnology, and precision manufacturing, ultrasound gel pads have demonstrated strong potential to advance coupling media for diagnostic, therapeutic, and wearable ultrasound applications, enabling higher diagnostic accuracy, streamlined workflows, and patient-centered care across diverse clinical and resource-limited settings.

Neurodegenerative diseases are difficult to detect during their pre‐symptomatic stages, when intervention is most effective. Advances in wearable biosensors, artificial intelligence (AI), and neuroscience now enable continuous monitoring of brain‐related physiological signals in real‐world settings. However, progress in sensing technologies, signal analytics, and machine learning remains fragmented, limiting the biological specificity, robustness, and clinical translation of many digital biomarkers. This review presents an integrated framework that links molecular pathology and neural circuit dysfunction to sensor‐detectable digital phenotypes across sleep, autonomic regulation, gait, speech, and ocular dynamics. We evaluate emerging wearable platforms, including bioelectric, optical, and neuromotor sensors, and discuss critical design considerations such as signal quality, user‐centered form factors, and ultra‐low‐power edge AI systems. We further examine how self‐supervised learning, federated AI, mechanistic modeling, and fairness‐aware validation can improve biomarker reliability, interpretability, and generalizability. To facilitate clinical deployment, we introduce the Personalized Brain‐Health Operating System (pBH‐OS), a privacy‐preserving reference architecture for scalable digital phenotyping. The framework maps conceptual layers to existing technologies while identifying key validation gaps. We also address practical challenges, including adherence, device dropout, informative missingness, regulatory compliance, and accountability. Together, these advances provide a pathway toward biologically grounded and regulation‐ready systems capable of identifying neurodegenerative risk years before clinical symptom onset.