Soft bioMechatronic exosuits represent a transformative class of wearable robotics designed to restore mobility and independence in individuals affected by neurological conditions such as stroke. Unlike traditional rigid exoskeletons, soft exosuits use lightweight, compliant materials and fabric-based actuators to deliver assistive torques that mimic natural muscle movements without impeding residual motor function. This study presents a comprehensive framework for the design and control of such systems, focusing on personalized gait assistance for stroke rehabilitation. The proposed system integrates soft pneumatic and cable-driven actuators, embedded inertial measurement units (IMUs), and surface electromyography (sEMG) sensors to enable real-time intention recognition and motion adaptation. A model-based adaptive control strategy is developed, leveraging human-in-the-loop optimization and gait phase detection algorithms to ensure phase-synchronized and user-specific actuation. The exosuit’s modular architecture supports individual calibration, promoting scalability across diverse patient profiles. Validation through clinical trials demonstrates significant improvements in gait symmetry, stride length, and muscle coordination, alongside reductions in metabolic cost. The integration of physiological feedback into control loops fosters neuroplasticity and active patient engagement. This research contributes to the advancement of soft wearable robotics as a viable, patient-centric tool for in-home and clinical gait rehabilitation, offering a promising path toward functional recovery.