In this paper, we consider two distinct challenges in the resolution of nonsmooth stochastic optimization. Of these, the first pertains to the pronounced dependence of dimension in Gaussian smoothing-enabled zeroth-order schemes, impeding applications to large-scale settings. Second, no unified analysis {exists} for smoothing-enabled stochastic zeroth-order methods, allowing for capturing standard and decision-dependent stochastic optimization. To contend with the first challenge, we introduce a new exponentially-shifted Gaussian smoothing {\bf esGs} estimator whose moment bounds enjoy a linear dependence on dimension (rather than a quadratic dependence as in standard Gaussian smoothing estimators). Second, we show that such an estimator can be extended in two distinct ways to address decision-dependent regimes where the underlying densities are either available in closed form or not. Notably, the resulting gradient estimators continue to display a linear dependence in dimension. We then develop an {\bf esGs}-enabled stochastic zeroth-order method applicable to nonsmooth strongly convex, convex, and {non-}convex regimes. Importantly, our guarantees provide improved dimension-dependence in iteration complexities (by a factor of problem dimension $n$) while maintaing similar oracle complexities. In addition, we also provide novel high probability guarantees and almost sure sublinear rate guarantees in convex settings, while a new subsequential almost sure convergence guarantee is provided in nonconvex regimes. Preliminary numerics support our theoretical findings show that the proposed schemes display improved computational times and more refined empirical accuracies.