Given the paper’s purpose, the author focuses on diagnosing and treating neurological diseases, emphasizing the recent development of neuroimaging technology in connection with deep learning. There is a greater need, based on the fact that the number of people with neurodegenerative diseases such as Parkinson’s and Alzheimer’s is on the rise, to come up with early diagnostic methods that would rely on machine learning and deep learning approaches. The progress shaped by high-end neuro-imaging devices, including most recently functional magnetic resonance (fMR) imaging, positron emission tomography (PET), and MRI, computer-based imaging techniques, has greatly aided the study of the brain’s structure and functional aspects in an unparalleled way. At the same time, machine learning techniques such as convolutional networks, recurrent networks and generative adversarial networks, among others, have helped solve the problems of operationalization and utilization of large volumes of images. This paper describes the basics of neuroimaging techniques used in clinical practice and research and how deep learning systems improve the application of these techniques in a more accurate and efficient diagnosis of various neurological diseases. Also, these technologies show that there are new perspectives in developing and managing these kinds of diseases. These perspectives have several benefits for diagnostics and therapy management, such as better treatment outcomes, patient-tailored approaches to treatment, and the possibility of real-time disease monitoring. This review highlights the revolutionary benefits of merging neuroimaging and advanced machine learning techniques for neurological healthcare delivery.