This review paper gives an overview analysis of the function of artificial intelligence (AI) in translation studies (TS). It examines four primary AI translation models: statistical machine translation, rule-based machine translation, neural machine translation, and hybrid machine translation. It evaluates the effectiveness of each version and explores its strengths and weaknesses, especially in handling figurative language (e.g., idioms, metaphors) and cultural nuances. The paper similarly explores avenues for boosting the performance of AI-based translation systems. Moreover, it addresses the moral and societal ramifications of AI in translation, encompassing problems related to AI representation in disciplines like literature and the arts. The paper also examines AI's capability impact on the interpretation profession, such as the demanding situations, opportunities, and dangers posed by AI-based translation, especially concerning professional obstacles, data privacy, and bias. Based on this analysis, the paper proposes recommendations for the current and future directions of AI in the field of translation.