This study integrates advanced artificial intelligence into bionic robotic systems to restore impaired musculoskeletal functions using biofeedback technologies. We developed a deep learning architecture to enhance control precision and mechanical design of a lower limb exoskeleton for improved rehabilitation. A convolutional neural network decodes surface electromyography signals from leg muscles, enabling real-time classification of movement intentions. Electromyography data from leg motions are processed with Short-Time Fourier Transform for feature extraction, and transfer learning improves classification accuracy. A multicriteria optimization model identifies exoskeleton mechanisms matching human anatomy while ensuring efficient force transmission and mechanical simplicity. A novel mechanism with closed kinematic chains enhances structural stiffness, load capacity, and specific power. The system uses a gravity-independent suspension and separated actuation strategy, reducing active drives and simplifying control by decomposing movement by degrees of freedom. It expends no power to support torso weight during horizontal motion, and a locking device sustains weight during the stance phase, conserving energy. Experimental prototypes show high electromyography decoding accuracy and responsive control, indicating potential for adaptive, precise rehabilitation assistance. This integrated approach–combining deep learning and rational mechanical design–offers effective, personalized rehabilitation for neurovascular gait impairments.