In the digital age, efficient categorization of news articles is crucial for personalized content delivery and enhanced user experience. This paper explores the necessity of utilizing machine learning techniques for news article category classification and conducts a comparative analysis between Transformer and Recurrent Neural Network (RNN) models to determine the most efficient approach. Through an extensive literature review, the paper discusses the challenges associated with manual categorization and highlights the potential of machine learning algorithms in automating this process. Specifically, the study focuses on comparing the state-of-the-art Transformer models, known for their attention mechanisms and parallel processing capabilities, with traditional RNN models, known for their sequential processing and contextual learning abilities. The evaluation encompasses a diverse dataset of news articles, considering 40 categories and 199,914 rows. The Transformers model implemented on the dataset yielded a validation accuracy of 71.92%, the GRU model yielded an accuracy of 65.79% and the Attention based LSTM model yielded an accuracy of 67.09%. This study provides valuable insights into selecting the most efficient model for this task and offers suggestions for further research in this domain. By leveraging the findings, news organizations and content platforms can streamline their processes, deliver more relevant content to users, and enhance their overall engagement and satisfaction.