In this era with the rapid growth in social media usage among the current generation, a huge amount of content and comments, most of them sarcastic, is seen. Sarcasm has turned out to be an important part of daily life, especially in news and social media, where sarcastic comments are often used for better attention. However, detecting sarcasm is always challenging because it deals with understanding the difference between what has been said and what is meant. The current paper focuses on the detection of sarcasm in news headlines with the help of deep learning. Previous works were based on a wide range of datasets; however, these had limitations regarding either size or quality. In this respect, the authors propose creating a new dataset of headlines from sarcastic news sites and real news sites that is large and of high quality, hence appropriate for machine learning model training. The authors have also used the CNN-BILSTM architecture for text analysis, identifying sarcasm expression and deciding whether it is sarcastic or not-sarcastic which gained an accuracy of 97%. This dataset is made publicly available to enable further research in this direction.