Fake news is experiencing rapid and growing progress in today’s world. This study provides an extensive comparison on false news authentication using machine learning approaches with an eye of enhancing the reliability and resilience of misinformation detection. With the exponential growth in digital news consumption, the spread of false news has become a major global problem. This work addresses this issue by building and testing five frequently used machine learning models: support vector machines (SVM), logistic regression, random forest, classification and regression trees (CART), and AdaBoost, in addition to a neural network architecture. For enhancing the performance of these models, hyperparameter tuning is performed using GridSearchCV, an organized method that exhaustively searches a defined parameter grid to find the optimal model configuration. The analysis shows SVM and neural network models excel in accuracy, while logistic regression and random forest perform robustly. AdaBoost and CART indicate moderate accuracy, highlighting overfitting tendencies. Here the experimental results provide light on the merits and shortcomings of each technique, emphasizing the trade-offs between model complexity, interpretability, and computing efficiency.