The detection of brain impulses and emotions has been a research topic of late. To define emotion as a psychological variable, various machine learning algorithms were applied but there are still some challenges with high dimensional data. To date, much research has been done on this topic to classify a maximum number of emotions and find clarity on the behaviors of the brain signal with certain stimuli. In this paper, we have thoroughly studied the data and experimented with various techniques to find out the best one and according to that we have designed the model to classify a maximum number of emotions in a short time. We have extracted various domains of features including time, frequency, wavelet, etc., and applied the Ensemble Feature Selection method to find out the best subset of features. This technique includes correlation technique, information gain measurement, and finally recursive feature elimination method to find the optimized feature set. For the classification part, various method has been reviewed and then a stacked ensemble generalization model has been adopted with respect to bagging and boosting results of state-of-the-art machine learning techniques. The results show that the ensemble approach of feature optimization (En-FS) combined with the stacked ensemble generalized model (SEn-G) classification performs better. The suggested technique has been tested using the DEAP Dataset, and the experimental findings support the efficacy of the strategy while also being compared to state-of-the-art approaches.