Machine translation (MT) has revolutionized language accessibility by eliminating communication barriers and enabling the global exchange of information across linguistic divides. Early MT approaches, such as Rule-Based Machine Translation (RBMT), relied on predefined linguistic rules and dictionaries, which allowed structured translation in controlled environments, particularly for critical applications such as scientific and government texts. The emergence of Example-Based Machine Translation (EBMT) introduced a translation-by-analogy method, leveraging extensive bilingual sentence pairs to enhance contextual accuracy and naturalness. Today, MT  plays a key role in making news and literature accessible across languages, allowing stories to reach wider audiences, and fostering a more informed, connected world. With MT, reports on local events and culturally significant literary works are made available internationally, bridging cultural divides and expanding the global narrative. This paper explores the RBMT and EBMT methods, examining their architectures, strengths, and contexts of application, especially for closely related languages such as Bengali and Hindi. Our findings highlight each approach’s role in advancing high-quality, accessible translation, particularly for low-resource languages such as Bengali and Hindi, thereby ensuring greater inclusivity and diversity in global news media and literature.