In this thesis, we investigate the results of gradient flows in Euclidean, metric, and Wasserstein space. Our primary objective is to provide a comprehensive and self-contained analysis of minimizing non-convex functions using the 2-Wasserstein gradient flow and some modified gradient flows. Firstly, we establish the equivalence between minimizing a continuous function in $\mathbb{R}^{d}$ and minimizing a relaxed functional $F[m]$ in the set of all probability measures in $\mathbb{R}^{d}$. Subsequently, we thoroughly examine minimizing the relaxed functional F[m] using the approach of gradient flows. To further enhance our understanding, we apply Swarm calculus and particle methods to numerically solve the gradient flow of $F[m]$.