The propagation of fake news in the digital age has raised momentous concerns about the integrity of information dissemination. This research aims to carry out a comparative assessment of machine learning algorithms for identifying fake news in social media. To achieve this, an attempt was made to collect a wide-ranging dataset of news articles, encompassing both real and fake sources. In the initial phase of the research, we preprocess the news data, extracting relevant features such as text content, metadata, and source credibility. Leveraging a wealth of labelled data, these models learn to differentiate between genuine and fake news articles, while also providing insights into the characteristics that contribute to their classification. In a world where information authenticity is paramount, the implementation of this system provides a valuable tool for discerning consumers of news and enables the swift identification of misinformation sources. The models were trained using four different datasets. Several machine learning algorithms were used for carrying out comparative assessment in terms of their efficiency and accuracy to detect fake news. Selected metrics were chosen for a comparative assessment. At the end of the experiments, for Dataset 1, the Passive Aggressive Algorithm had the highest accuracy of 0.8333, while for Dataset 2, the Passive Aggressive Algorithm had the highest accuracy of 0.914285. However, for Dataset 3, the AdaBoostClassifier Algorithm had the highest accuracy of 0.9142857. Furthermore, in Dataset 4, several algorithms which including Logistic Regression, Passive Aggressive, Ridge Classifier, SGD Classifier, AdaBoost Classifier, Bagging Classifier, Extra Tree Classifier, Gradient Classifier, Random Forest, NUSVC, Linear SVC, Decision Tree, BernoulliNB, and GaussianNB, all had the highest accuracy of 1.0000000. A bar chart was used to represent the results graphically, showing the minimum, maximum, and quartile range.