Air pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO₂ and SO₂ by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO₂, and SO₂ for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO₂ and SO₂); type (2) includes just lagged values of the output variables (NO₂ and SO₂). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract