Text classification is a very important research area in machine learning. Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. In spite of the growth and spread of AI in text mining research for various languages such as English, Japanese, Chinese, etc., its role with respect to Myanmar text is not well understood yet. The aim of this paper is comparative study of machine learning algorithms such as Naïve Bayes (NB), k-nearest neighbours (KNN), support vector machine (SVM) algorithms for Myanmar Language News classification. There is no comparative study of machine learning algorithms in Myanmar News. The news is classified into one of four categories (political, Business, Entertainment and Sport). Dataset is collected from 12,000 documents belongs to 4 categories. Well-known algorithms are applied on collected Myanmar language News dataset from websites. The goal of text classification is to classify documents into a certain number of pre-defined categories. News corpus is used for training and testing purpose of the classifier. Feature selection method, chi square algorithm achieves comparable performance across a number of classifiers. In this paper, the experimental results also show support vector machine is better accuracy to other classification algorithms employed in this research. Due to Myanmar Language is complex, it is more important to study and understand the nature of data before proceeding into mining.