In recent years, the expansion of sustainability reporting and disclosures related to the environment, society, and corporate governance has led to increased attention from researchers, regulators, and investors to the issue of greenwashing. Greenwashing occurs when companies use language, symbols, or environmental claims to create a positive image of their performance, while these claims are not consistent with their actual actions and performance evidence. This article, with a review and analytical approach, examines the role of natural language processing in identifying and explaining greenwashing in sustainability and environmental, social, and governance reports. The main goal of the research is to analyze the capacities and limitations of natural language processing-based methods in detecting misleading language, ambiguous claims, exaggerated statements, and inconsistencies between corporate narratives and actual performance of organizations. The research findings show that the mere use of positive and environmental vocabulary in corporate reports cannot be a valid indication of a company's true commitment to sustainability. In many cases, greenwashing is formed through the use of general phrases, promises without timing, passive verbs, emotional expressions and the lack of quantifiable evidence. Accordingly, traditional methods of text analysis or green word counting are not sufficient to detect greenwashing and more advanced approaches such as semantic analysis, sentiment analysis, action analysis, contradiction detection and intelligent language models should be used. In addition, the results show that purely textual models may in some cases reproduce misleading corporate narratives; therefore, connecting language analysis to real operational data and external evidence is of fundamental importance. On the other hand, the present study shows that the use of knowledge graphs and evidence-based verification frameworks can increase the accuracy of greenwashing detection. This approach allows comparing claims made in sustainability reports with data related to energy consumption, carbon emissions, waste management, supply chain and corporate governance policies. As a result, greenwashing detection goes beyond the level of lexical analysis and becomes an assessment of the relationship between claim, action, and evidence. The paper also emphasizes that effectively combating greenwashing is not possible through technology alone, but also requires strengthening green governance, green accounting, internal controls, information transparency, and managerial accountability. Overall, the results of this research indicate that natural language processing can be an effective tool for increasing the transparency of sustainability reporting and reducing the risk of greenwashing, but its effectiveness increases when it is used alongside structured data, knowledge graphs, multidimensional analysis, and monitoring mechanisms. Therefore, the future of research and practice in this area should move towards the development of native, interpretable, and evidence-based models; models that, in addition to text analysis, have the ability to measure the accuracy of companies' environmental claims in the real context of organizational performance.