Edge Cloud Computing is a key technology for enhancing mobile functionalities and real-time applications in devices with limited resources. This is done by sharing the resources of edge servers and offloading jobs to the edge cloud. In order to ensure a high-quality service and more efficient usage of resources, it is important not only to correctly configure the edge servers but also to carefully select where to deploy them. However, in Edge Cloud Computing there is a high amount of servers and, with the advent of 5G and Internet of Things, there will be a massive number of client devices as well. This would make the edge server deployment too complex to solve through convex techniques. In this situation, Machine Learning is the most appropriate approach. In this paper, we provide a deep analysis of the usage of k-Means Clustering and Particle Swarm Optimization in the edge cloud deployment problem. Our results show that the hyperparameters for these algorithms can significantly impact their running time as well as the efficiency of their results. Finally, we also provide how to best configure these algorithms for this specific problem.