Language Identification (LID) is a subset of Dialect Identification that addresses specific challenges and matters related to linguistic similarity between dialects. Various current approaches are used for dialect identification, but automated prediction is difficult because the clarity of voices is not in a perfect range, and inaccurate selection of features. It is essential to utilize an appropriate feature subset that contains sufficient signal information for the learning model to correctly recognize language dialects. So as to eradicate the mentioned issues, optimized stacking based ensemble learning is developed. The identification process initiates with the pre-processing by using an adaptive least mean square filter and a fractional bandpass filter. The features from the pre-processed audio signal will be extracted by using Gammatone frequency Cepstral coefficients (GFCC) and Shifted Delta Cepstral Coefficient (SDCC). Then, the extracted features will be reduced with the help of Independent Component Analysis (ICA). Furtherly, the classification of selected features will be further given to the Recurrent Neural Network (RNN), which acts as a meta-classifier and additionally gets information from a pair of distinct classifiers, such as Radial Basis Functional Neural Network (RBFNN) and Deep Belief Network (DBN). The hyperparameter present in the RNN classifier was tuned using the Deer Hunting Optimization Algorithm (DHOA). The proposed approach has an accuracy of 97%, a precision of 96%, also an F1-score of 97%. Therefore, for an automatic dialect identification, the suggested approach is the best option.