This research paper examines various Machine Learning approaches, including Natural Language Processing, sentiment analysis, and cluster analysis, for the intelligent classification of news articles. The paper emphasises data gathering, cleaning, and discovery, using statistical analysis and visualization methods. The method includes text preprocessing, N-gram analysis, and the use of several classification algorithms, including classical ML and Neural Networks. The distribution of news article categories is skewed, according to our study, so an N-gram analysis is required to gain a deeper understanding of textual patterns. At a maximum validation accuracy of 92.35%, the evaluation highlights the Naïve Bayes classifier’s superior performance over other machine learning algorithms in this context. This demonstrates how difficult it is to correctly categorize a variety of data. These results contribute to the problem-solving process and the direction of future research by deepening our understanding of machine learning applications in news article classification.