Machine Translation (MT) is the process of automatically converting text from one human language (the source language) into another human language (the target language) using computer software, without requiring direct human intervention. Owing to the rapid advancement of information and communication technologies, machine translation has become an indispensable tool in contemporary society. For resource-scarce languages such as Assamese, MT has emerged as a significant and timely area of research and development. With the expansion of modern technology, a vast proportion of scientific, technical, and academic knowledge is available primarily in English and other global languages. Translating this enormous body of information into Assamese through traditional human translation is both time-consuming and expensive. Consequently, the need for efficient and scalable machine translation systems has become increasingly important. In recent years, Assamese machine translation has witnessed notable progress through platforms such as Google Translate, Microsoft Translator, and the Government of India’s Bhashini (National Language Translation Mission) initiative. Although these systems have improved considerably, they are still far from flawless. Compared with traditional Rule-Based Machine Translation (RBMT) and Statistical Machine Translation (SMT) approaches, recent developments in Neural Machine Translation (NMT) and Large Language Models (LLMs) have demonstrated superior translation quality and contextual accuracy. Despite these advancements, the scarcity of high-quality digital linguistic resources and annotated datasets remains the most significant challenge for Assamese machine translation. This paper examines the current status of machine translation for the Assamese language, explores its prospects and potential applications, analyzes the linguistic and technical challenges arising from the grammatical structure of Assamese, and discusses future directions for research and development in this field.