Residential sector is one of the energy-consuming districts of countries that causes CO₂ emission in large extent. In this regard, this sector must be considered in energy policy making related to the reduction of emission of CO₂ and other greenhouse gases. In the present work, CO₂ emission related to the residential sector of three countries, including Indonesia, Thailand, and Vietnam in Southeast Asia, are discussed and modeled by employing Group Method of Data Handling (GMDH) and Multilayer Perceptron (MLP) neural networks as powerful intelligent methods. Prior to modeling, data related to the energy consumption of these countries are represented, discussed, and analyzed. Subsequently, to propose a model, electricity, natural gas, coal, and oil products consumptions are applied as inputs, and CO₂ emission is considered as the model’s output. The obtained R² values for the generated models based on MLP and GMDH are 0.9987 and 0.9985, respectively. Furthermore, values of the Average Absolute Relative Deviation (AARD) of the regressions using the mentioned techniques are around 4.56% and 5.53%, respectively. These values reveal significant exactness of the models proposed in this article; however, making use of MLP with the optimal architecture would lead to higher accuracy.