Machine Translation (MT) is a procedure in Natural Language Processing (NLP), where the automatic systems are used to translate the text from one language to another language without changing the meaning of source language. In this work, we provide our efforts in developing a rule-based translation system on the Analyze-Transfer-Generate paradigm which employs morphological and syntactic analysis of source language. We utilized shallow parser for Hindi language along with dependency parse labels for syntactic analysis of Hindi language, developed modules for transfer of Hindi to English and generation of English language. Due to wide difference in word order of the two languages (Hindi following SOV and English SVO word order), a lot of re-ordering rules need to be crafted to capture the irregularity of the language pair. As a result of drawbacks of the aforementioned approach, we shifted to statistical methods for developing a system. A wide variety of machine translation approaches have been developed in past years. As each model has its pros and cons, we propose an approach where we try to capture the advantages of each system, thereby developing a better MT system. We then incorporate semantic information in phrasebased machine translation using monolingual corpus where the system learns semantically meaningful representation.