As social media continues to shape modern communication, the rapid spread of fake news has become a critical issue. While existing solutions for fake news detection have made progress, there is a significant gap in addressing this problem for low-resource languages like Hindi. Most current research focuses on general algorithms without considering the potential role of sentiment analysis in improving detection accuracy. This paper introduces a novel approach that incorporates sentiment analysis into the identification of fake news, particularly in low-resource language contexts. Using the TALLIP Fake News Dataset, various models are evaluated for their performance. Our approach achieves an accuracy and F1 score of 89%, demonstrating a 3% improvement over existing methods. This research addresses an important gap in the detection of fake news, particularly in underrepresented languages, and has the potential to reduce the spread of misinformation, thereby mitigating its impact on social issues.