The generalized approach to the problem of synthesis under uncertainty is to formulate if as a stochastic optimization problem that involves optimization of a probabilistic function obtained by sampling over uncertain variables. The computational burden of this approach can be extreme and depends on the sample size used for characterizing the parametric uncertainties. A new and efficient approach for stochastic process synthesis is presented. The goals are achieved through an improved understanding of the sampling phenomena based on the concepts derived from fractal geometry. A new algorithm for stochastic optimization based on these concepts to accelerate the process of synthesis under uncertainty is presented. Apart from the benchmark HDA synthesis problem, a real-world problem of synthesizing optimal waste blends is analyzed to rest the applicability of this novel approach in addressing the general problem of synthesis tinder uncertainty. The solution of this real-world large-scale synthesis problem is presented under uncertainty through the application of the new stochastic annealing algorithm, which takes into consideration novel sampling methods used in probabilistic analysis of process models.