High-throughput sequencing (NGS) offers high speed, cost-effectiveness, and wide applications in fields like biology and medicine. It has catalyzed multiomics integration, combining genomics, transcriptomics, proteomics, and metabolomics, leading to holistic research on biological processes and disease mechanisms. Drug target prediction benefits significantly from these advancements, with methods including similarity-based approaches, deep learning, and network-based techniques. The emergence of databases like CMap and LINCS has revolutionized this field by providing extensive transcriptional data, facilitating applications such as target identification and mechanism discovery. Furthermore, AI-based tools like Target2 and SSGCN have improved prediction accuracy by integrating multiomics data. In drug repositioning, multiomics-driven signature matching identifies drugs that reverse disease states, combining computational methods like molecular docking, network analysis, and supervised learning frameworks. As omics data expands, the integration of AI will further enhance drug discovery and repositioning efficiency.