<![CDATA[Brain tumours are among the most common malignant tumours, making their accurate detection and precise evaluation crucial for effective treatment planning and strategic regimens. Recent advancements in machine learning (ML) and deep learning (DL) have significantly increased tumour identification precision, enabling the automatic pro cessing of complex imaging data and substantially reducing the needfor time-consuming manual intervention. However, persistent challenges in automated detection approaches stem from pervasive imaging artifacts, variations in image quality, and diverse tumor appearances. This comprehensive review addresses these challenges by highlighting key innovations and their clinical relevance across various automated approaches, including clustering, soft computing, and deep learning techniques for the classification and segmentation of brain tumours using magnetic resonance imaging (MRI). Furthermore, we synthesize the quantitative results of state-of-the-art models, summarizing performance measures such as the Dice Score and Sensitivity. Ultimately, this review outlines the critical future research pathways necessary to effectively address remaining obstacles and enhance the precision of automated segmentation and classification.]]>
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