As one of the most extensively used languages in India, Telugu has a sizable audience and a huge library of news articles. Predicting the categories of Telugu news items not only helps with efficient organization but also makes it possible to do trend research, advertise in a certain demographic, and provide individualized recommendations. In order to identify the most effective method for accurate Telugu news category prediction, this study compares and contrasts various machine learning (ML) techniques, including support vector machines (SVM), random forests, and naive Bayes. Accuracy, precision, recall, and F1-score will be utilized as performance indicators to gauge how well these algorithms perform. The outcomes of this comparative analysis will address the particular difficulties and complexities of the Telugu language and add to the body of knowledge on news category prediction. For Telugu-speaking consumers, the study intends to improve news organization and recommendation systems, giving them more relevant and customized news consumption experiences. Our result emphasize that, although other models can be taken into account for further research and comparison, W2Vec-skip gram with polynomial SVM is the best performing combination.