Over the past decades, speaker identification has gained the attention of many researchers and security companies because of its many applications in identifying individuals. Therefore, through this work, a speaker identification system has been designed and implemented. The system undergoes a preprocessing phase that involves the removal of silence, the removal of outliers, the quantization of features, and the extraction of linear predictive coding (LPC) and Mel-frequency cepstral coefficients (MFCC) features. Additionally, the system performs a mean and standard deviation analysis on all features. The third phase involved applying deep learning techniques such as convolutional neural networks (CNN), artificial neural networks (ANN), long-short-term memory (LSTM), and random forests (RF). The proposed work's novel idea is a hybrid architecture, generated from ANN and LSTM. The proposed hybrid speaker identification system exhibits exceptional processing efficiency and achieves a remarkable accuracy rate of 94.63% and 99.2%, respectively. This study makes a substantial contribution to the advancement of speech recognition technologies by highlighting the adaptability and practical value of the hybrid ANN-LSTM model, especially in situations where speed is of the essence. This work was applied to a large dataset that was combined from three different sources: TIMIT, Prominent Leaders, Fluent Speech Command, and the GSCC dataset, which are all comprised of audio files only.