Every year, elections are held in India to choose new government officials. In the modern era, it’s common practice to try to guess the results of elections months in advance. In the past few years, social media sites like Twitter, WhatsApp, & Facebook have grown to be major information resources. These sites provide an environment where anybody having internet access may voice their thoughts and ideas. Predicting the results of the Indian general election using sentiment analysis is the focus of this study. The purpose of this study is to analyze the efficacy of many machine learning algorithms for forecasting the results of elections through the analysis of the sentiments of tweets about political organizations. A number of different machine learning techniques, such as “Logistic Regression (LR)”, “Random Forests”, “Gradient Boosting (GB)”, “SVM”, “Multinomial Naive Bayes”, and a suggested hybrid theory, were applied. The effectiveness of the algorithms was measured by their ability to correctly predict the results of the elections. The assessment results indicated that the suggested hybrid model had the highest accuracy (88.93%) compared to the other methods.