Non-coding RNAs, encompassing microRNAs, long non-coding RNAs, and circular RNAs have transitioned from being viewed as genomic “dark matter” to being recognized as pivotal regulators of gene expression, controlling vast transcriptional landscapes and cellular processes. Extensive evidence now links the dysregulation of these molecules to the pathophysiology of numerous human diseases, including cancer, cardiovascular disorders, and neurodegenerative conditions, thereby offering significant therapeutic and diagnostic potential. To navigate the complexity of these interactions and harness their potential, artificial intelligence is playing an increasingly crucial role. This chapter explores this emerging synergy, reviewing Machine Learning and Deep Learning approaches applied to predict miRNA–disease associations, small molecules–miRNA, lncRNA–miRNA and circRNA–miRNA interactions. While substantial progress has been made, significant challenges remain. We will therefore delve into both the advantages and the current limitations of these AI methodologies, aiming specifically to promote the critical development of more transparent and interpretable models that can be effectively utilized by clinicians and pharmaceutical chemists in real-world applications.