The proliferation of fake news poses a significant challenge in the digital era. Detecting false information, especially in non-English languages, is crucial to combating misinformation effectively. In this research, we introduce a novel approach for Dravidian fake news detection by harnessing the capabilities of the MuRIL transformer model, further enhanced by gradient accumulation techniques. Our study focuses on the Dravidian languages, a diverse group of languages spoken in South India, which are often underserved in natural language processing research. We optimize memory usage, stabilize training, and improve the model’s overall performance by accumulating gradients over multiple batches. The proposed model exhibits promising results in terms of both accuracy and efficiency. Our findings underline the significance of adapting state-ofthe-art techniques, such as MuRIL-based models and gradient accumulation, to non-English languages to address the pressing issue of fake news.