Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has had a substantial impact on a variety of fields, including healthcare, neuro-oncology, and precision medicine. In recent years, the availability of large-scale labeled datasets has allowed AI-driven advances in glioma detection, classification, and prognosis prediction. However, issues remain in assuring model generalizability, interpretability, and real-world clinical application. One of the most significant disadvantages is the underrepresentation of rare glioma subtypes, which prevents appropriate classification and therapy optimization. This study thoroughly assesses AI-based approaches for glioma classification, survival prediction, and biomarker discovery. A comprehensive survey of ML and DL models published between 2015 and 2024 has been conducted, evaluating radiomics-based tumor detection, multi-omics data integration, and AI-assisted decision-making frameworks. The review investigates the usefulness of convolutional neural networks CNNs, support vector machines SVMs, ensemble learning, and hybrid AI architectures, focusing on classification accuracy, sensitivity, and clinical applicability. Despite these advances, AI-driven glioma research faces challenges such as dataset consistency, clinical validation gaps, and a scarcity of explainable AI (XAI) frameworks. This paper offers a comparative analysis of artificial intelligence approaches assessing their strengths, constraints, and clinical relevance in glioma diagnosis and prognosis prediction in order to solve these challenges. The results underscore artificial intelligence’s revolutionary capacity in redefining glioma diagnosis, enhancing accuracy, and shaping the future of personalized treatment, thereby integrating computational progress with clinical neuro-oncology. Glioma diagnosis, deep learning, astrocytoma, oligodendroglioma, glioblastoma.