The field of genomic data engineering has been revolutionized by artificial intelligence (AI), enabling efficient storage, processing, and analysis of massive biological datasets. AI-driven techniques enhance the accuracy of genome sequencing, accelerate biomedical research, and facilitate personalized medicine. However, managing and processing genomic data presents challenges related to computational complexity, data security, and scalability. This research explores AI-based methods for optimizing genomic data storage, processing pipelines, and predictive analytics. The study highlights the role of deep learning, cloud computing, edge AI, and Salesforce-driven data management solutions in advancing genomic research, offering insights into future trends in biotechnology innovations.