This paper explores the practical applications of level proximal subdifferentials in variational analysis and control theory, focusing on their role in handling nonsmooth optimization challenges and enhancing system stability. By examining their use in optimizing complex systems and ensuring robust control under uncertainty, the study demonstrates how level proximal subdifferentials improve adaptability and accuracy in real-world scenarios. Key applications include stability analysis in dynamic systems, adaptive control, and constraint handling. The paper also discusses computational challenges and proposes future research directions to broaden their applicability in high-stakes fields.