Generative AI is a transformative technology capable of producing human-like textual content by training advanced language models on vast datasets. This paper explores the contributions of Generative AI in creative writing, content generation, and scientific research. The study emphasizes popular models such as Transformers, GANs, and BART, analyzing their effectiveness, computational complexities, and ethical implications. Using evaluation metrics like BLEU, ROUGE, and perplexity, this research provides a comparative analysis of model performance and limitations. Key findings highlight the need for mitigating biases, improving text diversity, and integrating ethical frameworks. Future directions include enhancing model interpretability and advancing legal guidelines for responsible AI deployment.