<p>The technique of producing more data from a small corpus to improve the predic- tion models’ performance is text augmentation. This Work Focuses on the pivotal role of text augmentation in Natural Language Processing (NLP). It tackles two significant challenges within the field: first, the adaptation of augmentation techniques for low-resource languages, where labeled data is scarce, and second, the enhancement of text classification across diverse domains, including senti- ment analysis, topic classification, and spam detection. This research leverages state-of-the-art transformer-based models like BERT and GPT-2 to ensure the adaptability and effectiveness of these augmentation techniques. The goal is to make NLP more accessible and impactful for low-resource languages, overcoming the challenges of data scarcity. Accuracy and applicability of text classification models, catering to a wide range of applications. Using two Swedish datasets as a paradigm for low-resource languages, we demonstrate the effectiveness of our techniques through thorough empirical testing, as measured by F1 scores. Our findings highlight how enhanced data improves classification performance in sit- uations with limited resources. By exploring various augmentation methods and their applications, this research contributes to advancing NLP solutions for both language-specific and classification-related challenges, pushing the boundaries of text augmentation’s capabilities in the field of NLP.</p>