<p>Natural Language Processing (NLP) primarily focuses on understanding and processing human language in a manner that machines can comprehend. NLP covers a wide range of tasks that enable machines to interpret and generate human language. These include identifying entities in text (Named Entity Recognition), answering questions, summarizing documents, classifying text, extracting and retrieving information, translating between languages, analyzing sentiment, and recognizing spoken language. Machine Translation (MT) has evolved from rule-based systems in the 1940s to sophisticated neural architectures that achieve near-human performance on high-resource language pairs. However, significant challenges remain in low-resource languages, domain adaptation, and evaluation standardization. This systematic review synthesizes 77 years of MT research to (1) quantitatively compare the evolution of MT approaches, (2) identify performance trends across different methodological paradigms, (3) analyze evaluation metrics and benchmark datasets, and (4) characterize research gaps, particularly for low-resource languages, including Indian regional languages. Following the PRISMA guidelines, we systematically searched eight academic databases (IEEE Xplore, ACM Digital Library, ACL Anthology, SpringerLink, arXiv, and others) from 1948 to 2025. Our search strategy included controlled vocabulary and Boolean operators, resulting in 1,354 initial results. After applying the inclusion/exclusion criteria and quality assessment, 164 studies were selected for final analysis. We extracted quantitative performance metrics and methodological details and conducted a comparative synthesis of Statistical Machine Translation (SMT), Knowledge-Based Machine Translation (KBMT), Neural Machine Translation (NMT), and hybrid approaches. Several researchers in the machine translation domain have attempted to translate sentences of different languages, views of newspapers, selections of epics, children’s stories, and novels into English, and translations of simple English sentences into Indian languages. However, they have not found sufficient accuracy in the translation of uncommon words, phrases, idioms, and sayings, or proverbs of Indian and non-descript proverbs. Thus, there is a major necessity to develop a viable machine translation scheme to translate several Indian and foreign languages (including Arabic, German, Japanese, French, and Russian) into English and vice versa. The field requires standardized evaluation protocols, expanded multilingual benchmarks (particularly for Indian languages, where only 23 studies exist), improved reproducibility practices, and a focus on practical deployment challenges. Our comprehensive benchmark comparison and identified research gaps provide a roadmap for future MT development, emphasizing the critical need for low-resource language support and robust evaluation frameworks.</p>