Numerical Weather Prediction (NWP) models are essential tools for forecasting atmospheric weather conditions and mitigating weather-related hazards. This research investigates the integration of certain atmospheric and weather parameters (rainfall, temperature, relative humidity, cloud cover, geomagnetic storms, and solar radiation) into NWP models to enhance the forecasting accuracy to a high degree. This research evaluates different NWP approaches, including deterministic and ensemble models, focusing on the parameterization schemes and data assimilation techniques. The NWP model was selected due to its flexibility in regional forecasting. Simulations were conducted using different parameterization schemes to ascertain the impact of geomagnetic and solar influences on weather conditions. This research employed data assimilation techniques, including 4D-Var, to integrate real-time observations into the model. The results demonstrate forecast reliability that was improved by a reasonable percentage by the addition of solar and geomagnetic influences, contributing to improved disaster preparedness and climate risk management and control. A stronger correlation (R = 0.85) between the modeled and observed cloud cover was obtained when both solar and geomagnetic influences were incorporated. The outcomes of this research are expected to offer valuable insights for improving weather forecasting accuracy, strengthening early warning systems, and enhancing preparedness for potential weather-related disasters, thereby providing more robust and effective risk and hazard management strategies. This study underscores the role of comprehensive atmospheric modeling techniques in strengthening early warning systems and reducing the uncertainties in weather forecasts.