Through the use of Convolutional Neural Networks (CNN) has demonstrated great success in handling uncertainty in image classification tasks. Applying optimization algorithms to a CNN can significantly improve the model’s structure, leading to enhanced performance. In this model, the Particle Swarm Optimization (PSO) algorithm was employed to refine the CNN structure. PSO was specifically used to identify optimal hyperparameters, among them the number of convolutional layers and the filters within each layer, thereby maximizing the model’s capabilities. Facial Emotion Recognition (FER) has become focus of ongoing exploration among researchers due to its wide-ranging applications across various fields. To validate the results, the FER2013 dataset was utilized.