Rainfall forecasting is a complex and critical problem faced by many meteorologists. Traditional forecasting models often struggle to capture both seasonal variations and long-term trends in rainfall data, making it essential to develop more robust methods. This study aims to propose a parallel hybrid forecasting model for rainfall prediction. The Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt-Winters Additive (HWA) models are used for parallel hybridization, with optimal weights determined by the variance-covariance matrix method. The model is evaluated using monthly rainfall data from January 1990 to December 2017 for Northeast (NE) India, divided into five divisions based on rainfall patterns: West Bengal and Sikkim (WBS), Arunachal Pradesh (AP), Assam and Meghalaya (AM), Gangetic West Bengal (GWB), and Nagaland, Manipur, Mizoram, and Tripura (NMMT). The proposed model outperforms individual models, including SARIMA, HWA, Holt, Exponential Smoothing (ETS), and Feed Forward Neural Network (FFNN) models across all regions. In the WBS region, it achieved an RMSE of 0.0798, an MAE of 0.0453, an MSE of 0.0063, an sMAPE of 0.3939, a correlation of 0.9414 between actual and predicted values, and an NSE of 0.8855. Similar significant reductions in evaluation criteria were observed in the other regions. The findings suggest that this hybrid model can be instrumental in improving rainfall predictions, especially in regions prone to extreme weather variations. By combining models that address both linear trends and seasonal fluctuations, this approach goes beyond existing efforts in the literature, offering a more accurate and reliable solution for rainfall forecasting. These results have significant implications for flood and drought management, climate change adaptation, and agricultural planning, particularly in the context of increasing climate variability.