Radiogenomics is a field that combines medical imaging with genomic data to improve our understanding of diseases. This chapter looks at how artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), helps analyze complex radiomic data to develop biomarkers that could identify or predict genomic artifacts. AI enables efficient extraction, selection, and segmentation of imaging features, which can be used for better disease diagnosis, prognosis, and treatment planning. We also explore how AI models integrate data types, such as imaging and clinical and genetic information, to predict disease progression and treatment responses. This chapter covers applications in various diseases, including cancer, heart conditions, metabolic disorders, and neurodegenerative diseases. We also discuss how combining radiogenomics with other fields like proteomics and metabolomics can provide deeper insights into disease mechanisms.