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Background: The duration of response to treatment is a major prognostic factor, and early relapse (ER) strongly predicts inferior survival in multiple myeloma (MM). However, the definitions of ER in MM vary from study to study and how to dynamically integrate risk distribution is still unsolved. Methods: This study evaluated these ER definitions and further investigated the underlying relationship with static risk distribution in 629 newly diagnosed MM (NDMM) patients from the National Longitudinal Cohort of Hematological Diseases in China (NCT04645199). Results: These data indicated that early relapse within 18 months (ER18) after initial treatment was the best time point for identifying early progression and dynamic high‐risk in MM. The ER18 population (114 of 587, 19.4%) presented with more aggressive biologic features and the inferior response to treatment compared to a reference cohort (p <.001), with a significantly short median overall survival (OS) of 28.9 months. Multivariate analyses confirmed the most significant prognostic value of ER18 on OS in the context of International Staging System stage, elevated lactate dehydrogenase, thrombocytopenia, cytogenetic abnormalities, and treatment (hazard ratio, 4.467; p <.001). The authors also described the specific transitions from static risk profile to dynamic risk distribution and then constructed a mixed‐risk‐pattern to identify four novel populations with distinct survival (p <.001). Additionally, the authors proposed a second‐state model that predicts dynamic risk changes, enabling a complementary role to the Revised International Staging System model in facilitating individualized systematic treatment. Conclusions: Collectively, this study concludes that ER18 is a simple and dynamic prognostic predictor in MM. In addition to static risk assessment, dynamic risk plays an important role in survival prediction. The present study sheds light on the optimal time point for early relapse (ER), defined as relapse within 18 months after initial treatment, and highlights its significant dynamic predictive impact on the prognosis in multiple myeloma. Alongside the conventional static risk stratification at diagnosis, a second‐state model that incorporates ER reveals dynamic risk changes that facilitate individualized systematic treatment. [ABSTRACT FROM AUTHOR]