Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates and long-term disabilities. Early diagnosis, particularly within the first hours, is crucial for preventing irreversible damage and improving patient outcomes. Although neuroimaging techniques like magnetic resonance imaging (MRI) have advanced significantly, traditional methods often fail to fully capture the complexity of brain lesions. Deep learning has recently emerged as a powerful tool in medical imaging, offering high accuracy in detecting and segmenting brain anomalies. This review examines 61 MRI-based studies published between 2020 and 2024, focusing on the role of deep learning in diagnosing cerebral vascular occlusion-related conditions. It evaluates the successes and limitations of these studies, including the adequacy and diversity of datasets, and addresses challenges such as data privacy and algorithm explainability. Comparisons between convolutional neural network (CNN)-based and Vision Transformer (ViT)-based approaches reveal distinct advantages and limitations. The findings emphasize the importance of ethically secure frameworks, the inclusion of diverse datasets, and improved model interpretability. Advanced architectures like U-Net variants and transformer-based models are highlighted as promising tools to enhance reliability in clinical applications. By automating complex neuroimaging tasks and improving diagnostic accuracy, deep learning facilitates personalized treatment strategies. This review provides a roadmap for integrating technical advancements into clinical practice, underscoring the transformative potential of deep learning in managing neurological disorders and improving healthcare outcomes globally.