Semantic relations between words in a sentence are identified using a technique called dependency parsing. Algorithms called dependency parsers are used to map the words in a sentence to the said semantic roles and to identify the syntactic relations between words. Transition-based dependencies on Indian languages have been relatively less explored, partially attributed to lack of high quality treebanks. Graph Neural Network based parsing techniques have shown relatively high accuracy levels on English language and languages with similar syntactic structures (eg. Spanish), and have potential to show good accuracy with other language syntaxes. Hindi is a language with an extremely rich morphology and a FreeWord order. Such a language, also referred to as a Morphologically Rich, FreeWord Order (MoR-FWO) language, is immensely difficult to parse using traditional methods. Using Graph Neural Networks, we present a state-of-the-art dependency parser for Hindi. We compare performance and efficiency of two approaches in this project- a Machine Learning model and a Neural Network model.