Describing the relationship between two or more random variables is a fundamental problem in statistics and probability. This paper, which is organized in two parts, provides a historical and structural review of dependence measures from the 18th century to the present. Part I covers foundational concepts: early definitions of independence and dependence in the works of de Moivre, Bayes, and Laplace; the emergence of correlation in Galton's work and its mathematical formulation by Pearson; the limitations of the product-moment correlation coefficient; and the development of nonparametric rank-based coefficients. The role of copula theory and Sklar's theorem in separating the dependence structure from marginal distributions is also explained. Part II addresses modern measures and generalizations: measures of intensity of dependence, non-monotone dependence and modern measures for high-dimensional data, serial dependence and autocorrelation, tail and quantile dependence, multivariate and vector extensions, measures for discrete and mixed data, applications in probability theory, and implementation in statistical software. This paper serves as a comprehensive resource for researchers in statistics, data mining, and machine learning by providing a review of both classical and modern measures.