For many decades, Machine Learning made it possible for humans to map the patterns that govern interpolating problems and also, provided methods to cluster and classify big amount of uncharted data. In recent years, optimization problems which can be mathematically formulated and are hard to be solved with simple or naïve heuristic methods brought up the need for new methods, namely Evolutionary Strategies. These methods are inspired by strategies that are met in flora and fauna in nature. However, a lot of these methods are called nature-inspired when there is no such inspiration in their algorithmic model. Furthermore, even more evolutionary schemes are presented each year, but the lack of applications makes them of no actual utility. In this chapter, all Swarm Intelligence methods as far as the methods that are not inspired by swarms, flocks or groups, but still derive their inspiration by animal behaviors are collected. The applications of these two sub-categories are investigated and some preliminary findings are presented to highlight some main points for Nature Inspired Intelligence utility.