Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/2789 2019 2019 Principal, Indur Institute of Engg. & Tech, Siddipet, Bharat, India Rajeev Rajeev Junior Engineer (Computer Science), Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, India Tushar Kumar Pandey Principal, Princeton Institute of Engineering & Technology for Women Hyderabad, Telangana, India, 4Professor, CMR Engineering College, Hyderabad, (T. S.), India. Rajeev Shrivastava Professor, CMR Engineering College, Hyderabad, (T. S.), India. Rajesh Tiwari Asst. Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, Bharat, India S. Anjali Devi Asst. Professor, Dept. of ECE, St. Martin's Engineering College, Secunderabad, Telangana, Bharat, India Neerugatti Varipallay vishwanath To better understand disease susceptibility and prevention, computational genetic epidemiology is leading research. This paper introduces "GenomeMinds," a breakthrough method for scaling large-scale AI models for disease risk prediction. HPC was used to develop the method. GenomeMinds is compared to six standard methods to demonstrate its benefits. GenomeMinds' incredible potential is shown by real-world performance assessments. These measures evaluate data processing speed, forecast accuracy, scalability, computer efficiency, privacy, and ethics. GenomeMinds benefits are shown via scatter plots, which visually compare data. According to the data, GenomeMinds may revolutionize computational genetic epidemiology by doing well across all criteria. GenomeMinds has faster data processing, better prediction accuracy, stronger scalability, higher computational efficiency, enhanced privacy and security, and a comprehensive ethical awareness. 2024 2024 182 190 10.54216/JCIM.130214 https://www.americaspg.com/articleinfo/2/show/2789