Voice recognition technology has evolved into a cornerstone in contemporary applications, from virtual assistants and intelligent homes to biometric security and healthcare. Its potential to facilitate unobstructed human-computer interaction has hastened the need for more precise and more robust systems. GMM and HMM traditionally paved the way for voice processing. However, deep learning architectures—namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based models—have revolutionized the field by achieving unprecedented performance and flexibility. This research paper provides a comparative study of major deep learning models for voice recognition, critically evaluating their design rationales, merits, and shortcomings Some major challenges like scarcity of data, noise tolerance, computational efficiency, and interpretability of models are discussed. The paper also offers the perspectives of the future trends in work like the inclusion of self-supervised learning, light-weight models for edge devices, and multimodal systems that will influence the next voice recognition generation. The goal of this work is to offer an end-to-end perspective of what exists currently and stimulate future work in this very dynamic field.