<p>Magnetic Resonance Imaging (MRI) is central to the diagnosis of neurological diseases such as Alzheimer’s disease (AD) and brain tumours, where early and reliable classification remains a pressing clinical need. In recent years, deep learning has transformed medical image analysis, with convolutional neural networks (CNNs), transformers, and hybrid models achieving unprecedented accuracy. However, despite these advances, challenges persist in areas such as generalisation across datasets, interpretability of model decisions, robustness to domain shifts, and the integration of multi-modal information. This paper makes two main contributions. First, we provide a comprehensive survey of deep learning methods for MRI-based classification of AD and brain tumours, covering CNNs, transformer-based models, hybrid designs, transfer learning strategies, ensemble approaches, and emerging paradigms such as federated learning and explainability. Second, we carry out a large benchmarking study on three public datasets: a Kaggle Alzheimer’s disease dataset with four cognitive-impairment stages, a four-class brain tumor dataset, and a seventeen-class brain tumor dataset. We test different model architectures and ensemble methods, and our results provide a clear experimental baseline for future work. For future research, we point out several important open challenges: improving model interpretability, increasing computational efficiency, reducing unfairness and bias, and integrating these tools into real clinical practice. We also suggest several research directions, including self-supervised learning on large neuroimaging datasets, domain-adaptive pretraining, lightweight ensemble distillation, and improved methods for uncertainty estimation. By combining an organized review of recent studies with a systematic benchmarking study, this paper is intended to be both a useful reference for researchers and a guide for advancing clinical AI in neuroimaging.</p>