Weather dynamics of Relative Humidity (RH) are notoriously nonlinear, there are outliers, and even the error distributions are asymmetric, all of which hinder the accurate prediction of RH in the conventional models. The limitations mentioned above are formulated in this study by introducing a random forest optimized with Aquila optimizer (RF-AO) as a novel hybrid machine learning model to mitigate these problems. The Aquila Optimizer improves generalization and Metropolitan noise robustness by adapting the RF hyperparameters such as tree depth, node splits, and ensemble size to meteorological data noise. The RF-AO model was run using daily RH data (2015–2018 from Pahalgam, India, provided by IMD) the RF-AO model reduced the Mean Absolute Error (MAE) to 0.1764 (vs. 8.8863 for standalone RF) and achieved a Willmott’s Index (WI) of 0.9901 d R2an of 0.9843 during testing. These improvements stem from the AO’s ability to balance exploration and exploitation during optimization, which mitigates overfitting and outlier sensitivity. The results demonstrate the model’s ability to be deployed for real-time applications in irrigation planning, HVAC control, and climate resilience strategy. Proposing a scalable framework for global climatic regions, this work integrates metaheuristic optimization into the ensemble forecasting for RH by making it more robust.