In order to arrange and evaluate this enormous amount of data, effective categorization techniques are now essential due to the exponential growth of digital news material. This study investigates the use of Large Language Models (LLMs) and other Natural Language Processing (NLP) approaches for the classification of news data. We study how well LLMs do automatic news article classification into predefined classes or subjects. We show through experimental evaluation that LLM-based techniques are capable of effectively classifying news data, providing valuable information about future directions and possible applications in this field.