The proposed system procures a systematic workflow in fake news identification utilizing machine learning classification in order to recognize and distinguish between real and made-up news. Using the Natural Language Toolkit (NLTK), the procedure starts with data preprocessing, which includes operations like text cleaning, tokenization, and stemming. This guarantees that the data is translated into an analytically-ready format. The preprocessed data is subsequently supplied into transformer models like M-BERT, Albert, XLNET, and BERT. By utilizing their extensive training on substantial datasets to identify complex patterns and significant traits that discriminate between authentic and false news pieces, these transformer models excel at capturing contextual information. The most successful model among those used is M-BERT, which boasts an astounding F1 score of 0.74. This supports M-BERT’s supremacy over its competitors in the field of fake news identification, outperforming them in terms of performance. The program can draw more precise conclusions and more effectively counteract the spread of false information because of its comprehension of contextual nuance. Organizations and platforms can strengthen their fake news detection systems and their attempts to stop the spread of false information by utilizing M-BERT’s capabilities.