Text summarization is a fundamental task in Natural Language Processing (NLP) that aims to condense large volumes of text into concise, meaningful summaries while preserving essential information. As digital content continues to grow exponentially, automated summarization techniques have gained prominence in various domains, including finance, healthcare, law, education, and journalism. By enabling faster information retrieval and improved decision-making, AI-driven summarization is transforming data-intensive industries. Summarization techniques can be broadly classified into extractive and abstractive approaches. Extractive methods identify and select key sentences from the original text, maintaining their structure, whereas abstractive methods generate new, coherent sentences that effectively capture the core meaning of the content. The advent of deep learning and Transformer-based models has significantly improved the performance of abstractive summarization, making it more human-like and contextually accurate. This paper explores the implementation of abstractive text summarization using PEGASUS, a state-of-the-art Transformer-based model optimized for summarization tasks. PEGASUS employs a unique pre-training strategy in which key sentences are masked, and the model learns to predict them, enhancing its ability to generate high-quality summaries. The proposed approach integrates PEGASUS into a FastAPI-based backend system, ensuring seamless text preprocessing, inference, and API-driven response generation. Key preprocessing steps include removing HTML tags, correcting spelling errors, and optimizing text structure for better model input. AI-powered text summarization offers significant benefits across industries. In finance, it enables analysts to quickly extract key insights from financial reports and market news. Healthcare professionals can efficiently summarize patient records and medical literature to improve clinical decision-making. In the legal domain, AI-driven summarization assists in condensing case laws, contracts, and legal documents, enhancing accessibility and research efficiency. News organizations and journalists can generate concise article summaries for faster content dissemination, while educational institutions can leverage summarization for summarizing textbooks, research papers, and lecture notes. Additionally, customer support services can optimize response times by summarizing inquiries and support tickets. With continuous advancements in AI and NLP, text summarization is set to revolutionize information processing, reducing manual efforts and enhancing productivity across multiple sectors. The integration of Transformer-based models like PEGASUS into real-world applications showcases the potential of AI in automating complex language tasks, making knowledge more accessible and actionable.