Skin lesion segmentation is a well-studied topic that faces obstacles such as differences in lesion shape, size, color, skin tones, and image noise. This study describes a U-shaped architecture designed for skin lesion segmentation. It uses ResNest blocks, Swin Transformer blocks, and Unified Attention methods integrated into skip connections. This CNN-Transformer hybrid model is effective in capturing both the local and global features. Squeeze attention and convolution are used in parallel in the bottleneck to balance the extracted local and global features, while the Unified Attention between the encoder and decoder blocks improves critical feature learning. The model’s effectiveness is proven through training and testing on three publicly available datasets: ISIC 2016, ISIC 2017, and ISIC 2018. Comparative analysis with state-of-the-art models indicates the suggested model’s remarkable performance, emphasizing its ability to delineate skin lesions precisely.