Voice Conversion (VC) is an important field of speech synthesis that focuses on changing a speaker's voice to sound like another person's voice while preserving the original speech content and meaning. This review summarizes fifteen key research studies that highlight the evolution of voice conversion technology, from traditional statistical approaches such as Gaussian Mixture Models (GMMs) and Conditional Restricted Boltzmann Machines (CRBMs) to advanced deep learning techniques including Sequence-to-Sequence (Seq2Seq) models, Generative Adversarial Networks (GANs), and fully convolutional networks. It also discusses major advancements such as one-shot voice conversion through representation disentanglement, the OpenVoice framework for instant voice style cloning, and raw audio generation methods that remove the need for conventional vocoders. In addition, the review explores the integration of Speech-to-Text (STT) systems and Natural Language Processing (NLP) for emotion analysis in applications such as mental health support and market research. Finally, it examines the performance of voice conversion systems through the Voice Conversion Challenges (VCC) and highlights the role of MOSNet, an automated evaluation model that provides speech quality assessments closely aligned with human judgments.