The presence of technology in every aspect of life has become almost inevitable. Technological progress is transforming the way we live and influencing our understanding and interpretation of the world around us. The progress in the field of machine learning, which has affected all layers of science and most industries, especially the fashion industry, is particularly evident. Therefore, the application of supervised machine learning in image classification with a focus on culturally specific dresses from different regions of the world becomes important. This approach aims to contribute to a better understanding and categorization of culturally specific clothing traditions, with potential applications in the fashion industry, education and the promotion of cultural diversity. The aim of this paper was to develop a model for image classification that can recognize and classify various dresses according to their origin. An image classification model was developed, using a dataset containing images of culturally diverse dresses from Scandinavia, the Mediterranean, the United Arab Emirates, and Central Europe. The model was trained on different numbers of epochs (50 and 100) to compare the results and performance of the model with respect to the number of epochs. The analysis of the performance of the model in relation to the number of epochs shows that the model trained on a larger number of epochs (100) achieves slight improvements compared to the model trained on a smaller number of epochs (50). A larger number of epochs (100) improves the performance in the classification of dress images characteristic of the United Arab Emirates and Scandinavia, while negatively affectting the classification of dress images characteristic of Central Europe and the Mediterranean. Both the model trained on 50 epochs and the model trained on 100 epochs have difficulties in classifying images of dresses characteristic of Central Europe, while they achieve the best results in classification of images of dresses characteristic of the Mediterranean. To achieve optimal results, further adjustment of the number of epochs and other hyperparameters is necessary. Also, increasing the number and diversity of the dataset, with the aim of better reflecting the different conditions and characteristics of the dresses, can significantly improve the model's ability to accurately classify images into all categories.