On February 23, 2022, the conflict between Ukraine and Russia broke out, shockingthe entire world and creating a heated debate on social media. This paper focuses on understanding the sentiments of Twitter users regarding the Russo-Ukrainian war and classifying them into appropriate categories. In this project, an eight-part dataset was utilized, consisting of various hashtags. Multiple classification techniques were applied, utilizing TF-IDF and Bag of Words vectorization. The dataset consisted of over 1.3 million tweets sourced from Kaggle, with a specific focus on hashtags like ‘Russian Troops’ and ‘Ukrainian War,’ among others. Preprocessing steps involved removing symbols and numbers, converting the text to lowercase, and applying stemming. After completing the preprocessing, conducting the analysis, and training the models, an accuracy of 96% was achieved on both the Logistic Regression and Decision Tree models