<p>Rainfall prediction plays a pivotal role in agricultural planning, water resource management, and disaster preparedness, particularly in regions like Mizoram, India, where rainfall patterns are complex and variable. However, achieving accurate forecasts remains a significant challenge due to the intricate interplay of seasonal and trend components. This study addresses this issue by evaluating the performance of three forecasting models—Additive Holt Winters (AHW), Multiplicative Holt Winters (MHW), and a Modified Holt Winters (MOHW) method—to enhance prediction accuracy for rainfall in Mizoram. Using historical rainfall data from 1986–2018 for training and data from 2018–2022 for testing, the models were assessed based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The testing results revealed RMSE values of 89.06, 645.94, and 87.76 for AHW, MHW, and MOHW, respectively, with MOHW consistently outperforming the other models in terms of accuracy and reliability. These results highlight the MOHW method’s superior ability to capture complex seasonal variations and trends, making it an effective tool for precise rainfall forecasting. The findings underscore the critical importance of employing advanced forecasting models like MOHW in regions with dynamic rainfall patterns. By providing more reliable forecasts, this study contributes to improved decision-making in agriculture, water management, and disaster preparedness, showcasing the potential of the MOHW method for broader applications in meteorological prediction and planning.</p>