This research aims to develop a Supporting Big Data and ML with a Framework for temperature forecasting using Artificial Neural Networks (ANN). The proposed framework utilizes a massive amount of historical weather data to train the ANN model, which can effectively learn the complex non- correlations that are linear with the parameters and temperature. The input variables include various weather parameters, such as humidity, wind speed, precipitation, and pressure. The framework involves three main stages: data pre-processing, model training, and temperature forecasting. In the data pre-processing stage, the raw weather data is cleaned, normalized, and transformed into a suitable format for model training. The data is then split into training, validation, and testing sets to ensure model accuracy. In model instruction stage, the ANN trained model using a backpropagation algorithm to adjust affected by the inherent biases and model based on the input and output data. The training process is iterative, and Using the validation, the efficiency of the model is measured. set to prevent overfitting. Finally, in the temperature forecasting stage, the trained ANN model is used to predict the temperature for a given set of weather parameters. The accuracy of the temperature forecasting is evaluated using the testing set, and the results are compared to other forecasting methods, such as statistical methods and numerical weather prediction models. The proposed framework has several advantages over traditional temperature forecasting methods. Firstly, it utilizes a vast amount of data, which enhances the accuracy of the forecast. Secondly, the ANN model can learn the interactions between the input variables that are not linear and temperature, which cannot be captured by traditional statistical methods. Finally, the framework can be easily extended to incorporate additional weather parameters or to forecast other environmental variables. The results of this research show that the proposed framework can effectively forecast temperature with high accuracy, outperforming traditional statistical methods and numerical weather prediction models. Therefore, it has the potential to improve weather forecasting and contribute to various applications, such as agriculture, energy management, and transportation.