In an era dominated by the rampant dissemination of misinformation, the paper’s primary objective is to develop a robust system capable of accurately detecting fake news, thereby enhancing the credibility and reliability of information. This paper compares the abilities of SVM classifier, Naive Bayes, and MaxEnt classifiers along with feature engineering techniques such as part-of-speech (POS) tagging, term frequency-inverse document frequency (TF-IDF), and trigram models to achieve higher accuracy levels. Three primary objectives underscore, this research: Unlike conventional methods reliant on sentiment analysis and fact-checking this paper aims in the development of a sophisticated text classification model leveraging advanced NLP techniques and neural networks, the systematic evaluation of baseline models such as Margin Maximizing Classifier, Naïve Bayes, and Maximum Entropy (MaxEnt) Classifier, and the implementation of feature engineering techniques to improve model accuracies through refined linguistic analysis. Through meticulous data analysis and comprehensive methodology, this research aims to contribute to the advancement of fake news detection strategies, thereby fostering informed and united communities. The research showcased the effectiveness of the MaxEnt model combined with TF-IDF and trigram features in detecting fake news, achieving a remarkable accuracy of 0.95. This superior performance underscores the model’s ability to capture complex linguistic patterns and word associations through a synergistic approach. The results also provide a foundation for future studies to integrate multimodal data sources and explainable AI, promoting transparency and trust in deployed fake news detection systems.