Cancer, characterized by its high heterogeneity and multifactorial nature, remains a leading cause of mortality worldwide. It results from a complex interaction of genetic mutations, epigenetic changes, and disrupted regulatory networks that collectively lead to uncontrolled cell growth, metastasis, and therapy resistance. MicroRNAs (miRNAs), a class of small noncoding RNAs that modulate gene expression at the posttranscriptional level, have recently been recognized as key players in both oncogenic and tumor-suppressive pathways driving cancer progression. Their capacity to affect multiple pathways at once makes them promising candidates for diagnostic, prognostic, and therapeutic biomarkers. With the introduction of next-generation sequencing (NGS), profiling miRNA expression has become more accessible and thorough. However, analyzing and interpreting these high-dimensional datasets necessitates computational frameworks that can capture complex biological interactions. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), provides robust methods for managing, analyzing, and extracting meaningful insights from large-scale miRNA sequencing data. This chapter examines how AI-driven approaches are revolutionizing cancer prognosis and treatment by utilizing miRNA data, focusing on integrative models, data-driven biomarker discovery, and clinical decision support systems.