The increasing circulation of misinformation and fake news in online environments poses growing and unpedictable risks to tourism destinations and geosites, where accurate communication is essential for visitor safety, heritage protection, and destination reputation. This problem is further intensified by the rapid and unchecked spread of digital content. This study develops a supervised machine-learning approach to identify deceptive content in tourism-related texts by combining domain-specific keywords extracted from the abstracts of the GeoJournal of Tourism and Geosites with a pre-labelled fake-news corpus. The text preprocessing phase utilized comprehensive Natural Language Processing techniques to clean the textual data, eliminate noise, and ensure high-quality feature extraction. Using Natural Language Processing techniques and five classification algorithms (logistic regression, Support Vector Machine, boosted decision tree, decision forest, and neural network), the models were trained and evaluated within the Microsoft Azure environment. Results show that simpler and more interpretable models, particularly boosted decision trees and logistic regression, outperform neural networks on the sparse, tourism-filtered dataset, achieving high accuracy, precision, and F1 scores. The performance evaluation was strictly validated through comprehensive confusion matrices and Receiver Operating Characteristic curves to compare overall classifier efficiency. The findings highlight the vulnerability of geosites to misinformation, especially regarding natural hazards, geomorphological features, and heritage narratives, and demonstrate the potential of AI-based tools to support reliable communication and decision-making in tourism management. The study provides a foundation for future multilingual and geospatially enhanced misinformation-detection systems that can strengthen the resilience and sustainability of tourism destinations and geosites.