This chapter delves into the concept of measurement information, exploring its definition, functions, properties, and quantitative evaluations. It covers the relationship between information and uncertainty, detailing syntactic, semantic, and pragmatic measures. A significant focus is on entropy, its properties, and its calculation for both discrete and continuous messages, including joint and conditional entropy. The paper also discusses redundancy and its impact on transmission efficiency, using mathematical models like the Shannon formula and Markov chains to analyze information sources. Practical applications in communication systems, data storage, and signal processing are considered, emphasizing the dynamic nature of information in societal and individual development. Through mathematical equations and real-world examples, the chapter provides a comprehensive framework for understanding and measuring information and entropy.