Abstract: As brain diseases can severely affect society, studies on the diagnosis of brain diseases are gaining importance. China is focused on counteracting the issues in brain disease diagnosis and treatment. Magnetic resonance imaging (MRI) has the advantages of high resolution and noninvasive nature, making it a preferred technique for brain disease research and clinical examination, providing rich databases for brain disease diagnosis. Deep learning is used in various fields due to its scalability and flexibility, and it has shown great potential for further development. Owing to recent developments in deep learning, it has made impressive achievements in various fields, such as computer vision and natural language processing, exhibiting great potential for its development and impact on brain disease diagnosis. Deep learning is being increasingly used for the diagnosis of brain disorders. We categorized studies reporting the use of deep learning for brain disease diagnosis by the type of disease to provide insights into the latest developments in this field. We cover the following aspects in this review. First, we reviewed and summarized the application of deep learning in the diagnosis of three typical brain disorders: autism spectrum disorder (ASD), schizophrenia (SZ), and Alzheimer’s disease (AD). Second, we reviewed commonly used datasets and available open-source tools for diagnosing these three brain disorders. Finally, we summarized and predicted the application of deep learning in the diagnosis of brain disorders. The review focused on the diagnosis of the aforementioned brain disorders. ASD is a neurodevelopmental disorder that occurs in early childhood. SZ is a psychiatric disorder that occurs in young adulthood. AD is a brain disorder that commonly occurs in old age. We illustrated the application of deep learning in the diagnosis of these brain disorders based on the characteristics of their different inputs. While using MRI as an input source, most convolutional neural networks were used as backbone networks to design feature extraction methods. However, while working with data containing sequence information from many time points, recurrent neural networks were used to extract key information from the sequences. Apart from directly processing images as input, many studies extracted manual features, constructed graphs of manual features, and used graph neural networks for analysis. This approach yielded remarkable results. Moreover, our findings indicated that graph neural network–based analysis methods are being commonly used to diagnose brain disorders.