The importance of fake news detection is pivotal due to its role in upholding the integrity of information propagation. In the contemporary digital landscape, where news circulates swiftly across platforms, the capability to discern and counteract fake news is essential for preserving well-informed societies. Fake news has the potential to disseminate misinformation, sway public sentiments, and trigger tangible effects. In this work, a fake news detection model is proposed using ML and DL models. This work uses a Kaggle dataset with fake news and true news for experimentation. We applied ML algorithms SVM, DT, RF and K-NN. Later, we also applied DL algorithms RNN, LSTM and ensemble of CNN&LSTM for fake news detection. The experiments shown that proposed CNN & LSTM ensemble model given good accuracy rate for fake news detection when compared to traditional models.