News article classification is a critical task in natural language processing that involves categorizing news content into predefined categories based on its textual features. Effective classification of news articles enables better organization, retrieval, and recommendation of information for readers and automated systems. This study presents a comprehensive approach to news article classification using both traditional machine learning techniques and advanced deep learning models. We evaluate the performance of algorithms such as Support Vector Machines (SVM), Naïve Bayes, and Convolutional Neural Networks (CNN) on a diverse dataset of news articles. The results demonstrate that deep learning models, particularly those utilizing word embeddings and convolutional layers, outperform traditional methods in terms of accuracy and precision. The findings underscore the potential of deep learning techniques in handling the complexities of news text and suggest avenues for further research in improving classification performance using hybrid models and domain-specific features.