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Global warming and climate change have become one of the most embarrassing and explosive problems/challenges all over the world, especially in third-world countries. It is due to a rapid increase in industrialization and urbanization process that has given the boost to the volume of greenhouse gases (GHGs) emissions. In this regard, carbon dioxide (CO₂) is considered a significant driver of GHGs and is the major contributing factor for global warming. Considering the goal of mitigating environmental pollution, this research has applied multiple methods such as neural network time series nonlinear autoregressive, Gaussian Process Regression, and Holt’s methods for forecasting CO₂ emission. It attempts to forecast the CO₂ emission of Bahrain. These methods are evaluated for performance. The neural network model has the root mean square errors (RMSE) of merely 0.206, while the Gaussian Process …