Nanobubbles (NBs) exhibit great application potential in water treatment owing to their large specific surface area, highly negative Zeta potential, and extended stability. However, the stability of the NBs is affected by the coupled interaction of multiple factors such as gas type, pressure, flow rate, medium, pH, and temperature. Conventional empirical trial-and-error approaches are inefficient, difficult to reproduce, and non-generalizable. To overcome these limitations, this paper established a data-driven inverse design framework combining interpretable machine learning and experimental validation to reveal key laws of NBs′ stability and propose transferable preparation strategies. A random forest model was optimized, achieving a precision of 0.816, a recall of 0.814, and an F₁ score of 0.810 on the test set. Model feature analysis indicated that ozone, carbonate or mildly alkaline media, and suitable pressure and temperature were the main factors improving the stability. Guided by inverse prescriptions, NBs retained strong stability after 21 days of storage, and in the system coupled with persulfate, the removal rate of methylene blue by NBs reached (96.8±2.3)%, outperforming the control group. Long-term performance tests further showed that the NBs/PMS system achieved about 51% TOC mineralization of natural organic matter (NOM) within 30 days, demonstrating sustained oxidative capacity under complex matrices. Energy consumption evaluation indicated that, for 90% methylene blue removal, the single-treatment energy consumption of the NBs/PMS system was approximately 35.7% of that required by the conventional UV/PMS process, corresponding to a reduction of about 64.3%. This result highlights its advantages in energy efficiency and cost control. This study not only provides design principles for regulating the stability and theoretically reveals the laws of multi-factor synergistic effect but also verifies its application potentials in terms of long-term stability and engineering energy efficiency. It is of great significance for promoting the large-scale application of NB technology and the industrialization of green water treatment.