This research investigates the automatic detection of humour. The basic structure of humor consists in the punchline and contextual meaning leading to the joke. Although a joke's pith lies in the punch line, it would be difficult to interpret one that doesn't have enough context. One of the biggest obstacles to humour recognition is that it is subjective. As a result, difficulties arise because it can be difficult to determine whether something should be considered humourous or not. Nonetheless, humour undoubtedly becomes easier to understand when this aspect of everyday communication is incorporated into machines. To investigate the automatic detection of humour, this research was carried out on 200k samples and uses deep learning architecture; RNN(LSTM) in conjunction with two pre-trained embeddings; GLOVE and FASTTEXT, BERT, DistilBERT and a machine learning classifier Naïve Bayes to classify and predict humour. The experimental findings suggest all models are effective for humour detection in text, with BERT achieving the highest accuracy of 97% as a result, explains the importance of contextual approach in detecting humour.