A recent advancement in medicine that has enormous potential for improving human health is referred to as "genomic medicine" more frequently now. This innovative method of healthcare identifies people who are more likely to develop certain diseases and intervenes earlier to prevent these diseases by using information of the genetic make-up of the individual. Finding the genes responsible for illness ethology will provide scientists the means to create more effective therapies and treatments. Predictive genomic medicine, which advocates screening healthy people to find those who have alleles that enhance their vulnerability to prevalent diseases like cancer and heart disease, is credited with playing a significant role in this discipline. Then, medical professionals could intervene even before the sickness manifests and provide them with advice.1As a first step towards genomic medicine, numerous nations have built databases on the DNA and health data of entire populations. Additionally, a sizable number of genes that could be used to predict a person's likelihood of getting a specific condition have been discovered through biomedical research. But since there are still numerous issues to be resolved, it would be naive to presume that genetic medicine will soon become a reality. Our understanding of the majority of illness genes and their functions is far insufficient to make accurate projections about a patient's likelihood of actually contracting the disease. In addition, new political, social, ethical, and economic problems brought on by genetic medicine will need to be resolved in the near future.AlphaFold can accurately predict 3D models of protein structures and is accelerating research in nearly every field of biology started in India 2016 . Currently, there are over 200 million known proteins, with many more found every year. Each one has a unique 3D shape that determines how it works and what it does.But figuring out the exact structure of a protein remains an expensive and often time-consuming process – and until now – scientists have only been able to study the exact 3D structure of a tiny fraction of the proteins known to science.Finding ways to close this rapidly expanding gap and predict the structure of millions of unknown proteins can not only help us tackle disease, and more quickly find new medicines, but perhaps, also unlock the mysteries of how life itself works.A model trained on the human genome, for example, was able to predict sites on RNA where proteins are likely to bind. This binding is important in the process of “gene expression” — the conversion of DNA into proteins. Specific proteins bind to RNA, limiting how much of it is then further translated into proteins. In this way, these proteins are said to mediate gene expression. To be able to predict these interactions, the model needed to intuit not just where in the genome these interactions will take place but also how the RNA will fold, as its shape is critical to such interactions.The generative capabilities of DNA language models also allow researchers to predict how new mutations may arise in genome sequences. For example, scientists developed a genome-scale language model to predict and reconstruct the evolution of the SARS-CoV-2 virus.Indian illness like Chikungunya and which affect villages and cripple people are less studied and these diseases of Global South needs specific advancements to the current mechanics. This abstract is to create awareness to conduct experiments on Indian illnesses using Indian speciment and hence hanvin a IndiGenAI for Genore sequences which not only caters for Indian illnesses but also considers indian participants in the experimentation. We want to bring awareness of AI scientists