The next generation of radio telescope arrays promises unparalleled sensitivity and resolution, unveiling a wealth of faint and diffuse radio sources. Conventional cataloging approaches struggle with the complexity of such data. To address this, we introduce Radio U-Net, a convolutional neural network based on the U-Net architecture, tailored for identifying diffuse radio sources, such as haloes and relics, in galaxy clusters. Trained on synthetic observations derived from cosmological simulations, Radio U-Net demonstrates robust segmentation capabilities, achieving high accuracy in both detection and morphological recovery of radio sources in large surveys.