Central nervous system (CNS) tumours, despite their low incidence, present significant clinical challenges due to their complex subtypes and poor prognosis, regardless of extensive research and advancements being achieved in the last decades and integrating artificial intelligence (AI) into neuro-oncology promises optimistically to revolutionise the diagnosis, treatment, and management of these tumours. To better understand the evolution of this landscape, computing literacy might become relevant among medical professionals and researchers shortly. AI technologies can excel in tasks such as image recognition and text analysis. Supervised ML models enhance tumour identification in MRI scans, while unsupervised ML models uncover hidden patterns in unlabeled data, aiding in tasks like clustering and dimensionality reduction. Reinforcement learning (RL) optimises treatment protocols through trial-and-error learning and feedback mechanisms. Deep learning (DL) has significantly advanced medical imaging and diagnostics with its neural network-based architectures, facilitating accurate tumour detection and segmentation. Natural language processing (NLP) and large language models (LLMs) automate documentation and extract information from medical records, improving clinical workflows. AI applications in neuro-oncology include enhancing diagnostic accuracy in neuropathology, optimising imaging techniques in neuroradiology, assisting in surgical planning and real-time decision-making in neurosurgery, refining radiation therapy protocols, and personalising treatment in clinical oncology. However, the considerable potential demonstrated in previous studies and the exponential growth of AI research underline the need of medical professionals able to address AI-related technologies and effectively collaborate with data scientists to improve patient outcomes. The complexity of this evolving research field requires rigorous validation before clinical deployment. This chapter highlights the importance of multidisciplinary collaboration and ethical considerations in integrating AI into neuro-oncology and speculates on its potential to transform patient care.