People are getting more exposed to fake news as their use of the web increases. to get notoriety while profiting from clickbait news outlets and social media, Though the spread of incorrect information has recently become more serious throughout the world, several existing techniques for categorizing and detecting have recently been created. When classifying news, the South Asian context is considered. More than 200 million people use Bengali as their first language, and it is their way of life. Communication necessitates are fundamental in Bengali. Our main intention of this research was to initiate an interpretation of the Machine Learning (ML) and Deep Learning (DL) assumptions. The machine learning classifiers (MLCs) are used and they are Random Forest, SVM, Decision Tree, XGB, Gradient boost classifier, and Ada boost classifier. The GB achieved the best accuracy, which was 89%. In addition, afterward, we used various well-known deep learning approaches to conduct our second stage of experiments. Where we have used RNN, LSTM, Bi-LSTM, GRU, and BERT. Then we comprehensively show the model's comparison to the product and the best evaluation result. There was also a comparative analysis to show the comparison of the otherward work. Which we think were beneficial in case of understanding the whole purpose behind our work. As Deep Learning methods were initiated, our model with the base of RNN has achieved overall 94% accuracy. By which we propose this article in the elaborate discussion of our full procedure.