Mango on an average account approximately 75 per cent of total production quantity. India is the largest mango producer, accounting for about half of the world-wide mango production. Forecasting of area, production and price fluctuations are the key to provide support in decision making and proper planning for sustainable growth of farmers and other people who are dependent on horticulture. The prices of mango are affected by cultivated area and yield of mango but in other ways pre or post-harvest management also affects it. The problems regarding the price fluctuations arise due to seasonality in arrival and its perishable nature. Therefore, the present study was carried out with time series intervention modelling in forecasting area, productivity and prices of mangoes. In the current investigation, simple exponential smoothing (SES) implemented to develop the forecasting models for area and productivity of mango. Under the SES, the error measurements at different values of alpha (a) for forecasting of area and productivity were observed that the value 0.8 and 0.9 of alpha (a) showed minimum Mean Absolute Percentage Error (MAPE) error ie 3.11 per cent, and 12.73 per cent, respectively. The study also developed time series ARIMA models for forecasting the prices of the mango (Keshar and Alphonso) for Valsad markets of Gujarat. It was showed that ARIMA (6, 1, 2) and ARIMA (1, 1, 2) were found good models for forecasting the prices of the Keshar and Alphonso, respectively in Valsad district of Gujarat