Early and accurate detection of brain tumours is crucial for improving patient survival and treatment planning. This study presents a framework based on deep learning for automated brain tumour detection and classification using magnetic resonance imaging (MRI) scans. A convolutional neural network (CNN) architecture was developed and trained on a curated dataset of labeled brain MRI images. This model can distinguish between tumour and non- tumour cases, as well as different tumour categories. The proposed model uses preprocessing techniques like image normalization, resizing, and augmentation to improve generalization and reduce overfitting. The network architecture includes multiple convolutional layers with ReLU activation, max-pooling operations, batch normalization, and fully connected layers, followed by a softmax classifier. We evaluated model performance using standard metrics such as accuracy, precision, recall, F1-score, and loss analysis. Experimental results show high classification performance, with strong accuracy and balanced precision-recall characteristics across validation and test datasets. Training and validation curves indicate stable convergence with minimal overfitting. A comparative analysis with baseline machine learning approaches confirms that the proposed deep learning framework is superior in feature extraction and classification robustness. The system has potential for real-time clinical decision support, helping radiologists with early tumour identification and reducing their diagnostic workload. This study highlights the effectiveness of convolutional neural networks in medical image analysis and supports their use in automated brain tumour diagnosis. Future work will focus on multi-class tumour grading and integration with transfer learning techniques. learning models, and validation on larger multi-institutional datasets to improve clinical applicability and scalability.