Particularly by leveraging multi-omics data, applying artificial intelligence (AI) in healthcare has resulted in a fresh approach to tailored therapy. Multiple omics thoroughly characterises and quantifies the many biological molecules that make up an organism, thereby analyzing its structure, function, and behavior. AI-driven techniques allow one to effectively combine genomes, proteomics, metabolomics, and Transcriptomics data. This provides a whole picture of a patient's health by exposing complex biological interactions and disease processes. Some of the most recent artificial intelligence techniques that simplify the integration of many kinds of genetic data so that tailored treatment regimens may be developed are discussed in this article. We discuss the most recent advancements in machine learning techniques able to manage big, varied datasets. We particularly pay close attention to deep learning models that can effectively forecast the course of illnesses and the efficacy of therapies. The report also examines cases of how multi-omics research driven by artificial intelligence has advanced the treatment of long-term diseases such as cancer, diabetes, and heart disease far ahead. Also discussed are issues including various kinds of data, difficulties merging them, and the requirement for robust computer systems. In the end, this analysis demonstrates how artificial intelligence might not only raise the accuracy of health diagnoses and prognoses but also generate tailored treatment plans for every patient depending on their own biological composition. By making treatments more precise, efficient, and tailored, the convergence of artificial intelligence and multi-omics will transform healthcare by which will raise patient outcomes and streamline healthcare services.