In this thesis the mammalian nervous system and mammalian brain has been used as inspiration to devlop a computational intelligence model based on the neural structur of fear conditioning and extends the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system insted of traditional models on the rational system of the human brain. The suggested model can be considered as a new data driven model and it is is referred to as brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain emotional system that are responsible to activate fear-response. In this thesis the model is initially investigated for prediction and classification. The performance have been evaluated by using various benchmark data sets from prediction applications e.g., sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of the performed tests, the models have been tested for both long-term and short-term prediction. The performance of BELIMs has also been evaluteb for classification, by classifying binary and multiclass benchmark data sets.