In this research, we go in depth into the intricacies of sarcasm detection within the domain of news headlines, using a carefully curated News Headlines Dataset. Leveraging CNN, BiLSTM, and an Attention Model, our methodology unveils a nuanced approach to identifying sarcasm. Through analyzing the linguistic nuances present in sarcastic statements, our model demonstrates increased sensitivity to local and global dependencies, which enhances the sarcasm detection accuracy. Experimental results on authentic datasets like the News Headlines Dataset show the effectiveness of our proposed method, thus showing the combined strengths of CNN and Attention BiLSTM in sarcasm detection and subsequent style transfer. This research contributes significantly to computational linguistics, offering a robust framework applicable to news headlines and providing novel insights into the adaptation of linguistic style for improved communication.