The encoding of temporal information is a basic function of biological systems that underlies perception, learning, decision-making, and behavioral synchronization. Despite an extensive array of empirical data and a variety of theoretical models, there is still no unified concept of temporal encoding. The report provides an overview of publications dedicated to research on the memorization, representation, and reproduction of time intervals by living organisms, as well as the modeling of time encoding. The modern understanding of neural correlates of time encoding is considered. The evolution of approaches and models is traced: from classical models of the internal chronometer to more complex network, population, and Bayesian concepts. Key trends and paradigm shifts in modeling memorization and prediction of time are outlined. The key properties of time encoding observed in most vertebrate species are indicated. Two fundamental models are described in detail: the internal clock model and the Scalar Expectancy Theory. Their significance for control theory, artificial intelligence, and robotics is justified.