There is a rising need for automated systems that can accurately identify misleading content because the internet is now flooded with inaccurate information. In this work, we examine how well the eight distinct state-of-the-art deep learning models-BiGRU, CNN, RoBERTa, and GraphSAGE-perform on the text-based fake-news classification problem. We evaluate the efficacy, generalization capacity, and limitations of the various models to categorize false information and documents from various writers (i.e., C2L Writers) over time across various forms using a variety of benchmark datasets. Our comprehensive studies demonstrated that models that combine transformers and graphs are far more efficient than conventional neural networks, particularly when it comes to identifying bogus news and comprehending material based on context and how it relates to other stuff. This work emphasize the significance of employing reliable feature extraction and representation learning techniques for scalable, real-time automated misrepresentation identification. Additionally, this study will establish baseline measurements for future development of false-document analysis utilizing automated machine-learning algorithms and deepen our understanding of various machine learning techniques for text-based fake news detection.