Fake news is misleading information which can often damage individual’s reputation and can change person’s opinion. Recently, the rise of social media platforms gave a boost to circulation of fake news across the globe. Our paper presents a machine learning model using voting ensemble with equal and different weights of each base model. We used 4 different classification algorithms as base learners- 1- Logistic Regression, 2-Multinomial Naive Bayes, 3- Passive Aggressive Classification Algorithm, 4- Random forest algorithm. All algorithms chosen works best with the long textual in stream of data. We compared accuracy of each base model. After assigning different weights to each base model, voting ensemble gave an accuracy of 93.5%.