We develop a unified framework for robust nonsmooth optimization problems with equilibrium constraints (UNMPEC). As a foundation, we study a robust nonsmooth nonlinear program with uncertainty in both the objective function and the inequality constraints (UNP). Using Clarke subdifferentials, we establish Karush–Kuhn–Tucker (KKT)–type necessary optimality conditions under an extended no–nonzero–abnormal–multiplier constraint qualification (ENNAMCQ). When the robust objective is ∂C-pseudoconvex and the active robust constraints are $\partial^C-$quasiconvex, we further provide sufficient conditions for global and local optimality. Building on these results, we derive
robust KKT-type necessary optimality conditions for weakly robust stationary points of UNMPEC. Sufficient optimality conditions for UNMPEC are provided under generalized $\partial^C-$pseudoconvexity and $\partial^C-$quasiconvexity assumptions using ENNAMCQ. Our results offer a systematic treatment of mathematical programs with equilibrium constraints that are simultaneously robust, nonsmooth, and equilibrium-constrained, in addition to verifiable tools for analyzing such models under uncertainty