Pineapple (Ananas comosus (L.) Merr.) is an economically important fruit crop in India with substantial regional production significance. Assam is the leading pineapple producing state of the country. Accurate forecasting of pineapple area and production is critical for agricultural planning and policy formulation. This research included trend pattern analysis and forecasting model development for pineapple area and production data of Assam. Four trendline models were developed for both the area and production series, out of which the logarithmic model was identified as the most suitable for pineapple area and exponential model for pineapple production. Three forecasting approaches were applied: auto-regressive integrated moving average model (ARIMA), artificial neural network (ANN) and non-linear support vector regression (NLSVR). A hybrid modelling framework was employed to integrate linear and non-linear components. Comparative evaluation showed that the ANN model outperformed ARIMA and NLSVR as standalone models, while the ARIMA-ANN hybrid outperformed the ARIMA-NLSVR hybrid for both series. The performances of all models were compared through root mean square error (RMSE) and mean absolute percentage error (MAPE) measures. For pineapple production, ANN model exhibited lowest RMSE and MAPE than among all evaluated models (ARIMA, NLSVR, ARIMA-ANN and ARIMA-NLSVR). On the other hand, hybrid ARIMA-ANN model emerged as best model for pineapple area series than the other four forecasting models. The findings of this study may support agricultural planners, policymakers and stakeholders in developing effective production strategies, resource allocation plans and long-term forecasting frameworks for pineapple cultivation in Assam.