A gas turbine (GT) produces energy by compressing air with fuel, ignition, and expansion. During this process, the GT produces toxic emissions. A predictive emission monitoring system mitigates the harmful effects of these gases on the environment by predicting the emission strategy and minimizing the emission levels. Neural networks, like convolutional neural networks (CNNs), can predict emissions from equipment using dimensional data. CNNs can extract features from data with a high degree of detail. By incorporating spatial and sequential data, CNN-bidirectional long short-term memory (BiLSTM) network attention regression models provide more accurate and consistent predictions of erratic emission patterns. As a result, the model achieved superior predictive accuracy compared with earlier approaches in terms of root-mean-squared errors (carbon monoxide [CO], 0.0639; nitrogen oxides [NOx], 0.0498) and coefficient of determination (R2) (CO, 0.819; NOx, 0.891). The research used cutting-edge machine-learning methods and technical expertise to create a system that would improve turbine efficiency and reduce emissions of greenhouse gases.