In this chapter, techniques used to optimize the imbalanced class and high-dimensional image datasets using swarm intelligence (SI) algorithms are proposed. Datasets converted from images or multimedia usually have problems of imbalanced class distribution and high-dimensional features. These problems seriously affect the accuracy and efficiency of image processing, especially in machine learning. Compared with other methods, SI optimization algorithms can simultaneously and stochastically solve these two problems in a search space. The SI optimization algorithm is a relatively new approach in the field of artificial intelligence. Specifically. the classical particle swarm optimization and contemporary bat-inspired algorithm are adopted in our experiment. Our proposed method achieved high reliability and high accuracy in classification performance from a computer simulation experiment. Moreover, it can synthesize more reasonable minority class samples as well as select the appropriate features when compared to the existing methods.