One of the first steps in the judicial process is finding the applicable statutes/laws based on the facts of the current situation. Manu- ally searching through multiple legislation and laws to find the relevant statutes can be time- consuming, making the Legal Statute Identi- fication (LSI) task important for reducing the workload, helping improve the efficiency of the judicial system. To address this gap, we present a novel knowledge graph-enhanced ap- proach for Legal Statute Identification (LSI) in Indian legal documents using Large Language Models, incorporating structural relationships from the Indian Penal Code (IPC) the main leg- islation codifying criminal laws in India. On the IL-TUR benchmark, explicit KG inference significantly enhances recall without sacrific- ing competitive precision. Augmenting LLM prompts with KG context, though, merely en- hances coverage at the expense of precision, underscoring the importance of good rerank- ing techniques. This research provides the first complete IPC knowledge graph and shows that organized legal relations richly augment statute retrieval, subject to being integrated into lan- guage models in a judicious way. Our code and data are publicly available at Github. (https://github.com/SiddharthShukla48/NyayGraph)