With the rapid advancements in artificial intelligence, hydrogel-based sensors, acting as the critical interfaces between biological organisms and the digital realm, are becoming essential for emerging human-machine interactions (HMI) and health-monitoring platforms. However, conductive hydrogels still encounter trade-offs between "conductivity-mechanics" coupling as well as reliability challenges in complex environments. Specifically, enhanced carrier pathways often compromise the polymer network strength and fatigue tolerance. In this study, we constructed a superior ionic-electronic conductive network by incorporating hierarchically self-assembled magnesium boride into a copolymer matrix under ultraviolet light irradiation. The hierarchical magnesium boride structures exhibit inherently high conductivity, while their anisotropic three-dimensional architecture promotes strong mechanical interlocking and multi-point coordination effects. These characteristics endow the composite hydrogel with robust adhesion (~200 kPa), remarkable stretchability (~1383 %), rapid responsiveness (<20 ms), and self-healing capability. Utilizing these advantages, the hydrogel enables high-fidelity physiological signal monitoring and versatile strain sensing, ranging from subtle pulse wave detection to Morse code recognition. Furthermore, a hand-wearable control system combined with a lightweight convolutional neural network (CNN) algorithm enables precision gesture recognition with an overall classification accuracy of 92.15% and stable control of drone posture. This work validates the reliability of hydrogel electronic skin in complex HMI scenarios, and lays the groundwork for future applications in hazardous environment operations.