Text augmentation is an active research area in natural language processing (NLP) that aims to improve model robustness, generalization, and performance by artificially increasing the diversity of training data. Traditional text augmentation techniques include synonym replacement, random insertion, random deletion, and back-translation, which generate paraphrased sentences while preserving semantics. More advanced methods leverage contextual embeddings from models like BERT, GPT, or word2vec for semantic-aware substitutions, as well as generative models such as variational autoencoders (VAEs), sequence-to-sequence networks, and large language models (LLMs) to produce realistic synthetic text. Research also investigates adversarial text augmentation for robustness against perturbations, mixup strategies for textual data, and domain-specific augmentation in low-resource languages, sentiment analysis, question answering, and dialogue systems. Automated augmentation frameworks like EDA (Easy Data Augmentation), AEDA (An Easier Data Augmentation), and paraphrase generation pipelines further streamline augmentation without heavy manual intervention. Current studies emphasize balancing semantic fidelity, diversity, and grammatical correctness in augmented text, establishing text augmentation as a key enabler for data-efficient and resilient NLP systems.