Stroke is a major health concern worldwide, and accurate diagnosis, treatment planning, and tracking of disease condition depends on the precise segmentation of brain stroke lesions. However, attaining high segmentation accuracy is hampered by the intricacy of lesion features in Magnetic Resonance Imaging (MRI) scans. Addressing these challenges is critical for improving the accuracy and reliability of stroke diagnosis. This review evaluates the impact of deep learning on improving lesion segmentation accuracy in post-stroke MRI imaging. It focuses on neural network architectures specifically designed to tackle stroke imaging challenges, such as convolutional neural networks (CNNs), U-Net, and Transformer-based models. From the use of sophisticated deep learning algorithms to the clinical difficulties in treating strokes, has been examined. The review also explores key datasets, including Ischemic Stroke Lesion Segmentation (ISLES) and Anatomical Tracings of Lesions After Stroke (ATLAS), which are instrumental in training and validating these models. The review concludes by identifying key challenges and possible directions for further research, emphasizing the need for methods that increase segmentation accuracy while accelerating their use in clinical practice. In addition to improving segmentation accuracy, these developments are expected to promote the state-of-the-art technologies into routine clinical practices, guaranteeing better treatment and outcomes for stroke survivors.