In the water industry, a state-of-the-art membrane bioreactor (MBR) requires relatively high operation expenditures due to foulants continually accumulating and clogging the surface and pores of the membrane. Hence, with the advent of digital innovations, developing an innovative tool based on data-driven approaches for membrane maintenance is important and challenging issue to achieve sustainable MBR operations. We developed a digitally-transformed early-warning protocol (DT-EWP) for membrane cleaning by predicting, diagnosing, and producing a warning for biofouling phenomena in a full-scale MBR plant. Biofouling progress was recursively predicted utilizing Kalman filter method and then performed diagnosis to identify the dominant fouling mechanism, incorporating genetic algorithm. In addition, membrane cleaning warning rule based on fouling-cumulative sum (FCUSUM) control chart was proposed to precautionary and gradationally produce an alarm for operational failure in the targeted plant. The proposed DT-EWP method was evaluated using the experimental data of a full-scale MBR plant in South Korea over a year. The results showed that the DT-EWP can estimate the biofouling progress of the trans-membrane pressure with R² of 97% as the highest degree of accuracy and extend operational lifespan of membranes by an average of 45.29%, while accomplishing qualitative operations by statistically controlling the operational failures.