Legged robots face challenges in maintaining stability, especially in dynamic and uncertain environments with visco-elastic contacts. This paper explores the implementation of reactive balance control strategies to enhance the stability of legged robots operating in such scenarios. By leveraging sensor feedback and real-time adjustment mechanisms, reactive balance control enables legged robots to adapt to changing ground conditions and disturbances, thereby improving their robustness and agility. This study investigates various approaches to reactive balance control, including feedback-based control algorithms, predictive modeling techniques, and machine learning methods. Through simulation studies and experimental validations, the effectiveness and performance of these strategies are evaluated in visco-elastic contact environments. The results demonstrate the potential of reactive balance control in mitigating instability and enhancing the locomotion capabilities of legged robots in challenging terrains.