Mango hopper, Idioscopus nitidulus is the most destructive pest of mango in the India. Thus, aim of the study was to develop precise and easy early population prediction model of mango hopper for tropical mansoon climate conditions. Weekly occurrence data of mango hopper, I. nitidulus during five consecutive years (2014 to 2018) was used for developing hybrid of multiplicative seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) model. The population of I. nitidulus increases in the month of January-February on flower panicles and October-November on new vegetative shoots in the region. The linearity in the time series data was best fitted with SARIMA (0, 0, 2) × (0, 1, 1)52 model as their correlation values are not outside the confidence intervals (CI) limits. Further ANN modeling was done for fitting the SARIMA residuals. The fitted values of model prediction and the actual values of year 2017-2018 flowering season (SMW36-52 of 2017 and SMW 1-13 of 2018) were used for testing of prediction efficiency. The performance of the two models in respect to model fitting and effectiveness of SARIMA and hybrid SARIMA-ANN model was compared by evaluating diagnostic statistics of MSE, RMSE, MAE and MAPE. The best fitted developed hybrid model in present study and the data predicted by model was matched with actual data of mango hopper incidence during the year 2017-18. Hybrid model developed in this study will help to predict hoppers population in advance, thus provide a direction for planning of timely prevention and development of effective management strategies which will help to minimize the use of hazardous pesticides.