AI can play a vital role in achieving net-zero emissions by identifying critical emission sources, assessing their properties and chemical composition, analyzing historical CO2e data, and building simulation models for emission identification and reduction. However, there are challenges in integrating real-time, semi-real-time, and manual data entries, setting up procedures for validation, auditing, and publishing accurate numbers. It is also difficult to identify, map, and monitor Scope 3 emissions regularly. Another opportunity is to analyze and predict energy demands, oil/gas flaring, combustion/indirect sources, and other associated emission categories. We have achieved primary success in establishing an effective roadmap for CO2e emission identification and forecasting.