Stochastic processes and random matrix theory (RMT) have developed into deeply interconnected fields with wide-ranging applications in physics, mathematics, statistics, and engineering. Stochastic processes provide a probabilistic framework for modeling time-evolving systems, while RMT studies the statistical properties of matrices with randomly distributed entries. The interplay between these areas has led to profound results, including universal laws for eigenvalue distributions, connections to growth phenomena, and new tools for high-dimensional data analysis. This article surveys the foundational concepts, key results, and applications arising from the intersection of stochastic processes and random matrix theory.