Phytoplankton over-proliferation and the associated algal blooms in lakes is a worldwide ecological challenge. In China, bloom control has predominantly relied on the Environmental Quality Standards for Surface Water, which emphasize total nitrogen and total phosphorus concentration controls. However, this concentration-based approach may yield high costs and low effectiveness, failing to achieve cost-efficient control under constrained management resources. The limiting factor theory offers a scientific basis for resolving this dilemma by identifying the key influencing factors of phytoplankton, thereby enhancing the precision and efficacy of management interventions. Grounded in Liebig‘s law of the minimum (governing final yield) and Blackman‘s law of limiting factors (governing growth rate), this study systematically elucidates the multiple, interacting mechanisms that constrain phytoplankton, including nitrogen, phosphorus, light, temperature, hydrodynamics, and food-web regulation. We review three principal methodologies for identifying limiting factors—empirical threshold, empirical modeling, and experimental approaches—and discuss their respective applicability and limitations. Furthermore, the modulating roles of relatively uncontrollable environmental drivers, such as light and temperature, on the effectiveness of nutrient reduction on algal blooms are elaborated. From a precision lake management perspective, we propose three key prospects: i) clarifying external nutrient control strategies, ii) establishing a “climatic potential-realized performance” framework to evaluate phytoplankton nitrogen and phosphorus assimilation efficiency, and iii) elucidating nutrient redistribution among trophic levels under fishing-ban policies. These insights aim to address critical management questions—what to control, how much to control, where to control, and how to enhance the ecological capacity of lakes. This study focuses on constructing a selection framework to elucidate the integration of classical limiting factor theory with China‘s practical lake management needs, thereby identifying the most cost-effective technical measures from the existing technology pool to support precise algal bloom control.