Lake eutrophication is a global challenge. The models of eutrophication response and watershed optimization control decision-making are the key to formulating economic and efficient action plans. However, the existing reviews of eutrophication model mainly focus on the single aspects of model development, case study, sensitivity analysis, uncertainty analysis, simplification and surrogate model. There is a lack of the summary of studies on the coupling of macro and micro aspects, such as the non-linear response, treatment decision and water quality improvement and long-term evolution of ecosystems. Therefore the models and methods of lake eutrophication response simulation and pollution-reduction optimization were summarized and analyzed in this study. The eutrophication models are divided into (a) data-driven statistical models, (b) causal-driven mechanism models, and (c) decision-oriented optimization models. Generalized statistical models including classic statistics, Bayesian statistics, and machine learning are often used on response relationships establishment, time series analysis, and spatio-temporal forecasting and early warning. The mechanism model consists of the hydrological, hydrodynamic, water quality, aquatic ecology, and other processes. It is usually used to simulate the change on different spatial and temporal scales, in conjunction with the watershed models. Among them, sensitivity analysis, parameter verification, and model uncertainty will cause high computing costs. The decision-oriented optimization model combines the mechanism model to form a “simulation-optimization” system, which derives a variety of methods such as stochastic optimization and interval optimization under uncertainty. It can deal with the cost of computing time through parallel calculation, simplification, and surrogate models. The challenges for future lake management were identified, including: (a) integration of the external input and the heterogeneity of the lake's nitrogen, phosphorus and algae; (b) improving the correlation and accuracy of optimal control decisions and water quality targets; and (c) exploring the long-term changes of lake ecosystems trajectory and driven factors. Several research focus were proposed to deal with the challenges, including (a) prediction of lake water quality response based on multivariate data fusion and machine learning algorithms; (b) upscaling or downscaling coupling of mechanism models based on biomass and action-driven agent-based model to express the interaction process of population at multiple scales; (c) machine learning algorithms and mechanism models are directly coupled or data assimilation to reduce simulation errors; and (d) multiple simulation models with different spatial-temporal scales are fused to achieve precise and dynamic optimization.