In this research, we compare and contrast many different AI-algorithms designed to improve the ability to spot false information on social media. In particular, it assesses the efficacy of Passive Aggressive Classifier [7], Hugging Face [5], and Support Vector Machine Algorithms [1][2][3][4][8]. Amid the increasing menace of misinformation on social media, the need for ef-fective fake news detection mechanisms cannot be overstated. The study begins with an overview of the algorithms under review, followed by an explanation of their application in fake news detection. This analysis then moves into a compar-ison mode, assessing each method according to several criteria including compu-tational complexity, accuracy, precision, and recall. The research goes further into the pros and cons of each model, illuminating how well they perform with various sets of data and varieties of disinformation. In order to create reliable and accurate false news detection systems, it is important to determine which algo-rithms are the most successful. The results of this comparison not only add to the body of knowledge on disinformation identification, but they also provide con-crete strategies for bolstering the trustworthiness of content shared on social me-dia.