This dissertation explores the development of intelligent wearable technologies and adaptive systems that measure physiological and physical data to enhance the interaction between humans and machines. It aims to advance Human-Machine Interfaces (HMIs) through the development of wearable technologies that prioritize precision, inclusivity, and user adaptability.  The first study focuses on an EOG-driven headband, a wearable HMI designed for precise and continuous control through non-invasive eye biopotential tracking. This system integrates advanced signal processing and real-time classification algorithms through mobile to address challenges such as noisy biopotential signals and inconsistent wireless control., demonstrating its utility in telesurgery and virtual reality applications. The second study introduces a Two-Camera Eye-Tracking System (TCES), a novel dual-camera setup optimized for eye movement and gaze tracking. By addressing anatomical variability and environmental challenges, the system ensures high accuracy and inclusivity, enabling precise control of any screen-based tasks and external controllers, including robotic arms. The application of advanced machine learning algorithms and data-driven control further enhances its adaptability, making it accessible for people, including individuals with physical disabilities. Finally, the third study presents a Multiplexed Sensing Suit, a full-body motion tracking system that integrates multi-sensor arrays with cloud-based machine learning system. This platform enables real-time monitoring and classification of complex human motions, with applications ranging from sports analytics to rehabilitation. By combining flexibility, scalability, and detailed motion analysis, this study advances the field of wearable HMI beyond its capabilities.  Through these contributions, this dissertation aims to bridge the gap between user intent and machine response, creating systems that are both practical and transformative. By integrating advanced sensing systems, adaptive algorithms, and scalable real-time infrastructure, the research highlights the potential for HMI’s promotion to foster continuous, natural, and user-centric interactions in everyday and professional settings.