Leveraging AI and Machine Learning for Emission Prediction, Forecasting, and Target Setting in Industrial Operations represents a transformative approach to managing environmental impact in large-scale energy enterprises like Saudi Aramco. This framework integrates a wide array of operational and environmental variables, including ambient temperature, fuel and flare gas consumption, production rates of key hydrocarbons such as oil, natural gas liquids (NGL), ethane, propane, and condensate, as well as water output and energy system metrics like steam generation, electricity consumption, and power import/export flows. By incorporating detailed compositional data of fuel and flare gases, the models significantly enhance the accuracy of emissions estimation.