This paper aims to develop intelligent Predictive Monitoring Emission Systems (PEMS) for three distinct case studies involving traffic, gasoline fuel tanks and large combustion plants (LCP). The underlying theme of pollutant emissions exists in all three case studies whereby the gases that are monitored are NO2, unburned hydrocarbons, and SO2. These pollutants can cause grievous harm to health, environment and infrastructure hence they are vital to be monitored. Emissions models are required because this will allow countermeasures to be taken in order to control the attributes that contribute to the emission of these pollutants. The datasets are collected online via database libraries, and consequently data preprocessing and data division are done. Back-propagation neural networks (BPNN) are first used to model the emission, and then to compare, generalized regression neural networks (GRNN) are used. From the results it is shown that GRNN models outperform BPNN algorithms for complex and nonlinear datasets, because of the underlying radial basis kernel transfer function. The RBF kernel has fewer numerical difficulties; one of it is that the kernel output is contained between 0 and 1; hence the solution provided by GRNN is stable, certain and localized. © 2011 IEEE.