Temperature prediction is critical for many industrial and everyday applications. Numerical Weather Prediction (NWP) models using high-performance computing is the most sought technique to forecast weather, including temperature. However, NWP is complex in nature and computationally expensive. In this paper, the temperature is forecast using data-driven Machine Learning techniques, which are not computationally intensive and are further accelerated using GPUs. Two deep learning models: A stacked Long Short-Term Memory (LSTM) and Random Forest Regressor (RFR), are developed and validated using the standard ERA5 data (at 850hPa, above the atmospheric boundary layer). In addition, the models are tested against the ground-level observations (inside the atmospheric boundary layer) for twenty different locations in India. The performance of univariate and multivariate models is also analyzed for the real-time dataset. Root Mean Square Error (RMSE) obtained by the LSTM and RFR are 0.47 and 0.23, respectively, for ERA5 data. When compared to the numerical weather prediction model - operational IFS, the RMSE using LSTM and RFR is smaller by 65% and 83%, respectively. The LSTM and RFR models forecast temperature with an average RMSE of 0.7 for the real-time data at twenty locations. The GPU-enabled LSTM model performed 64 times faster than the CPU-enabled model. The developed RNN models are made publicly available at https://github.com/arasuadrian/RNN-Models. © 2022 IEEE.