New research shows that the rise of fake news has translated into an estimated $39 billion annual loss in the global stock market. Fake news has become a pressing issue, particularly in low-resource languages like Bangla. This study introduces a dataset and methodologies for detecting fake news using machine learning techniques. We utilized both machine learning and deep learning methods, achieving notable improvements in accuracy by incorporating domain-specific fake news probabilities. Without these probabilities, the models achieved 70–80% accuracy, while incorporating this feature boosted performance significantly. These findings highlight the effectiveness of neural networks and statistical models in combating misinformation and disinformation.