High Dynamic Range (HDR) image reconstruction has gained prominence in a wide range of fields; not only is it implemented in computer vision, but industries such as entertainment and medicine also benefit considerably from this technology due to its ability to capture and reproduce scenes with a greater variety of luminosities, extending conventional levels of perception. This article presents a review of the state of the art of HDR reconstruction methods based on deep learning, ranging from classical approaches that are still expressive and relevant to more recent proposals involving the advent of new architectures. The fundamental role of high-quality datasets and specific metrics in evaluating the performance of HDR algorithms is also discussed, as well as emphasizing the challenges inherent in capturing multiple exposures and dealing with artifacts. Finally, emerging trends and promising directions for overcoming current limitations and expanding the potential of HDR reconstruction in real-world scenarios are highlighted.