<p> Despite the growing recognition of microRNAs (miRNAs) as critical biomarkers in cancer, current approaches to their analysis remain fragmented, disjointed, and poorly integrated with emerging computational advances. This lack of cohesion limits progress toward reproducible and clinically actionable biomarker discovery. To address this unmet need, we present a review that unifies the latest findings and tools in bioinformatics, machine learning (ML), and large language models (LLMs) for miRNA analysis in oncology, thereby bridging a significant methodological gap in the field. We begin by critically synthesizing, benchmarking, and evaluating algorithms, including miRDeep2 and DIANA-miRPath, within a functional pipeline that spans next-generation sequencing (NGS) data processing to multi-omics integration. Building on this foundation, we review ML-augmented layers incorporating supervised and deep learning (DL) algorithms, specifically support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), to enable robust miRNA signature identification, classification, and target prediction. Furthermore, we explore the integration of generative models and LLMs to support hypothesis generation and enhance reproducibility in biomarker discovery workflows. This comprehensive framework enhanced with artificial intelligence (AI) is contextualized through cancer-specific datasets, with particular emphasis on translational applications for early detection, prognosis, and therapy selection. By systematically organizing fragmented methodologies into a scalable and reproducible pipeline, our work provides a strategic roadmap to accelerate the development of miRNA-based precision cancer.</p>