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The medical industry in India has undergone substantial evolution driven by progress in technology and infrastructure. Yet, prominent hurdles remain when addressing critical problems like inadequate medical infrastructure for the nation's massive population, restricted accessibility, a deficit of qualified practitioners, and high-quality services. Artificial intelligence (AI)-based interventions offer the possibility to ease the burden on the Indian medical network; nevertheless, incorporating reliable AI into this domain is still obstructed by moral and legal barriers. The objective of this research is to critically analyze the contemporary state of AI system engineering in Indian medical care and its alignment with the moral and legal dimensions of AI, while also pinpointing the obstacles and prospects for implementing reliable AI within the Indian medical domain. This investigation assessed 15 papers chosen from an initial pool of 1136 papers collected across two digital databases, PubMed and Google Scholar, alongside project sites. This inquiry utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). The findings indicate that current publications primarily deployed traditional machine learning (ML) models and artificial neural networks (ANNs) for diverse operations, including drug discovery, disease monitoring networks, early sickness identification and diagnostic precision, and medical resource administration in India. This analysis uncovers a deficiency in the integration of reliable AI within Indian medical care and various obstructions linked to it. It examines pathways for engineering reliable AI within Indian medical environments, prioritizing patient protection, information confidentiality, and adherence to moral and statutory criteria.