The global rise in demand for environment friendly products has led companies to adopt sustainability in the supply chain of consumer goods. However, few companies have resorted to unethical means and malpractices such as false advertising to increase the profit margin and thus deceive the public. This phenomenon, known as “greenwashing” involves misleading the public by making unsubstantiated claims about a product's environmental benefits. Greenwashing not only erodes consumer trust but also hinders genuine efforts towards sustainability and can lead to increased consumption of products that are not genuinely sustainable, exacerbating the problem of overconsumption and waste. In recent years, there has been a shift towards the digital era with the rise in technologies like artificial intelligence, data science, natural language processing, and many more. Researchers across the globe have been trying to solve this problem of greenwashing by utilizing these technologies. This paper aims to integrate the current technologies and research findings to devise a solution that effectively tackles greenwashing. A literature review has been conducted on more than 40 papers to determine the gaps in their findings and analyze them to conceptualize a framework using artificial intelligence and machine learning. The framework uses natural language processing to analyze the data provided by companies in their sustainability reports and compares the data with advertisements to verify their credibility. This tool helps prevent fraudulent malpractices and wrongful information dissemination to the general public by the companies involved. This, in turn, will ensure optimum resource utilization and curbing greenwashing practices, therefore, aiding in decarbonization and upholding consumer trust.