Abstract: This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature, pressure, and wind speed, typically calculated from computationally intensive weather research and forecasting (WRF) model. Accurate meteorological data is indispensable for simulating the release of radioactive effluents, especially in dispersion modeling for nuclear emergency decision support systems. Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming. Therefore, a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction. A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters. Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure, temperature, and wind speed components in both East-West and North-South directions. The performance of developed network was evaluated on an unknown dataset, and acquired results are within the acceptable range for all meteorological parameters. Results show that ANNs possess the capability to forecast meteorological parameters, such as temperature and pressure, at multiple spatial locations within a grid with high accuracy, utilizing input data from a single station. However, accuracy is slightly compromised when predicting wind speed components. Root mean square error (RMSE) was utilized to report the accuracy of predicted results, with values of 1.453 ℃ for temperature, 77 Pa for predicted pressure, 1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component. In conclusion, this approach offers a precise, efficient, and well-informed method for administrative decision-making during nuclear emergencies.