This literature review explores the use of web crawlers and page tagging for analyzing website data and measuring web trends. It examines various techniques and algorithms used in web crawling, including machine learning, mathematical models, and sentiment analysis. The review also discusses the challenges and limitations of web crawling, such as data security, scalability, and the need for accurate data processing. The goal is to understand how web crawling can be used to improve business productivity by providing insights into customer behavior, market trends, and competitor analysis.