Accurate segmentation of the retinal blood vessels is crucial for the early recognition and monitoring of numerous retinal illnesses, including age-related macular degeneration, diabetic retinopathy, and retinal vein occlusion. The intricacies of retinal images, such as precisely identifying thin arteries and controlling noise, can be challenging for traditional segmentation techniques. In this study, advanced deep learning approaches like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and U-Net improve the separation of retinal vessels and, as a result, detect retinal diseases. The goal is to present a novel method for deep learning that combines attention mechanisms, such as spatial and channel-wise attention modules, with multi-scale feature extraction methods such as pyramid pooling or dilated convolutions. By capturing contextual data at both the local and global levels, the deep neural network architecture will improve the identification of vessels with different widths and levels of complexity. To guarantee stable performance on various retinal image datasets, the model makes use of supervised learning in conjunction with sophisticated data augmentation approaches. This method provides a trustworthy instrument for automated retinal image processing and helps with the early detection and monitoring of retinal disorders by precisely segmenting retinal vessels. This study emphasizes the value of deep learning techniques in clinical applications for early illness identification and treatment planning, particularly CNNs, RNNs, and U-Net, and underlines their promise in retinal vascular segmentation. The findings show notable gains in segmentation sensitivity, specificity, and accuracy, especially in the identification of diseased areas and small arteries. To further improve clinical value, future research will concentrate on network architecture optimization and real-time segmentation capability exploration.