Modern biomedicine has generated molecular and clinical data that are no longer readily analysed within the framework of an older culture. Today, genomic, transcriptomic, proteomic, metabolomic and epigenomic measurements are routinely generated alongside electronic health records and continuous signals from wearables, and the challenge has shifted from a lack of data to principled interpretation. This review examines how artificial intelligence, particularly machine learning, connects multi-omics data to clinical decision-making. It outlines statistical and deep-learning principles, reviews early, intermediate, and late integration strategies, and highlights applications in oncology, cardiovascular medicine, neurology, chronic disease prevention, and infectious-disease surveillance. Technical performance alone is insufficient; validation, interpretability, equity, and regulatory compliance ultimately determine whether a promising model becomes a usable clinical tool. The review concludes with a plan for translation and the challenges that remain between the promise of the computer and lasting benefit in the clinic.