There are limitations with the conventional modeling of quiescent nonisothermal crystallization kinetics of fast-crystallizing polymers. A novel modeling of quiescent crystallization kinetics, the embedded artificial neural model (ANM), is proposed, which is based on the same concept used in obtaining conventional models-that is, the Avrami equation, the Nakamura model, and so on. In this approach, ANM plays significant roles in capturing the nonlinear relationship between temperature-crystallinity variables and the rate of crystallization kinetics. The temperature and crystallinity as well as the rate of crystallization kinetics are known through measurements during a nonisothermal experiment in differential scanning calorimetry (DSC). With this knowledge, the suboptimal weights and biases of ANM can be obtained by systematically minimizing a performance index based on the error between outputs from DSC data and outputs from ANM. The nonisothermal induction time model is used to specify the starting time of the crystallization process when the temperature of the polymer is below the equilibrium melting temperature. The temperature lag between the temperature of polymer sample and the temperature of the DSC furnace is considered in this study. As simulation results, the proposed modeling with predictive ANM can be used to describe the quiescent nonisothermal crystallization kinetics of fast-crystallizing HDPE in the presence of primary and secondary crystallization.