<p>Accents are an integral aspect of speech which reflects the cultural, regional, and linguistic diversity. However, differences in accent can make conversation and speech communication difficult, and in global contexts like language learning and human computer interaction. Accent conversion tends to tackle these problems by converting the accent of a speaker into the one of other speakers while preserving original linguistic content and speaker identity. This technology holds important applications to language learning, speech recognition, and cross-lingual communication. Accent conversion involves various steps such as speech analysis, feature extraction, feature mapping, and speech synthesis. The naturalness, quality, and flexibility of the accent conversion system has been substantially enhanced due to recent advancement in deep learning. Yet, challenges remain in modification of prosody, real-time processing, and preservation of speaker individuality. In this article, we present a comprehensive overview of state-of-the-art technologies in accent conversion, from traditional statistical models to modern deep learning approaches. Additionally, we discuss the methods applied for performance evaluation of these systems. We also present the current challenges, possible applications, and future directions in this evolving field.</p>