In recent years, deep network-based hashing has emerged as a prominent technique, especially within image retrieval by generating compact and efficient binary representations. However, many existing methods tend to solely focus on extracting semantic information from the final layer, neglecting valuable structural details that encode crucial semantic information. As structural information plays a pivotal role in capturing spatial relationships within images, we propose the enhanced image retrieval using Multiscale Deep Feature Fusion in Supervised Hashing (MDFF-SH), a novel approach that leverages multiscale feature fusion for supervised hashing. The balance between structural information and image retrieval accuracy is pivotal in image hashing and retrieval. Striking this balance ensures both precise retrieval outcomes and meaningful depiction of image structure. Our method leverages multiscale features from multiple convolutional layers, synthesizing them to create robust representations conducive to efficient image retrieval. By combining features from multiple convolutional layers, MDFF-SH captures both local structural information and global semantic context, leading to more robust and accurate image representations. Our model significantly improves retrieval accuracy, achieving higher Mean Average Precision (MAP) than current leading methods on benchmark datasets such as CIFAR-10, NUS-WIDE and MS-COCO with observed gains of 9.5%, 5% and 11.5%, respectively. This study highlights the effectiveness of multiscale feature fusion for high-precision image retrieval.