Natural language processing (NLP) challenges involving sarcasm comprehension are equally significant, especially when it comes to understanding sarcasm in communication—where verbal engagement, such as tone, gestures, and even facial signals, are absent, as in the case of text-based conversations. Though English sarcasm detection has improved significantly, transferring English-based techniques remains largely unused for other languages like Punjabi, Marathi, or Bengali. To evaluate the gap, this study assesses the ability of high-performance sarcasm detection models designed for any English-based language when applied to multilingual datasets of Twitter conversations in Punjabi, Bengali, and Marathi. The text was shocking to read, and the moderation of emotions, tokenization, and simplification pipeline (Grover and Banati in Heliyon 10:e36398, 2024 [1]) from scratch stands in strong support to increase any form of accuracy. To improve the correct ranking, these models analyze the target’s sense of sarcasm representation in devices for phonological dissonance, the disintegration of syntax structure, and the inversion of emotion polarity. The study also explored various deep and machine learning approaches based on hybrid models leveraging CNNs. Subtle sarcasm in the shape of deep-seated criticism tends to get missed by these models by acknowledging the fact that sarcasm is not likely to be heard in the target language alone by recognizing contextual sequences and local patterns using long-term memory-based deep learning. Also, these models attempt to use cultural and linguistic variables.